https://www.explorationpub.com/Journals/edht Most Recent Articles : Exploration of Digital Health Technologies. en-us Sun, 19 Apr 2026 11:07:52 GMT https://www.explorationpub.com/Journals/edht/Article/101161 Digital mental health interventions (DMHIs) have demonstrated considerable potential to address mental health needs across diverse populations by offering scalable, adaptable, and cost-effective solutions. Nevertheless, their implementation in the Global South—particularly among Indigenous peoples and traditional communities—remains scattered and insufficiently systematised. These communities frequently face structural inequalities, limited access to formal mental health services, and distinct sociocultural frameworks that necessitate the development of culturally appropriate digital interventions. To date, existing reviews have focused predominantly on high-income countries, highlighting the urgent need to synthesise context-specific evidence from low- and middle-income settings. Therefore, this protocol outlines the design of a scoping review aimed at examining the available evidence on DMHIs targeting Indigenous peoples and traditional communities in the Global South. The scoping review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews, and the results will be reported in line with the PRISMA-ScR guidelines. It will include articles resulting from primary research, systematic reviews, and opinion papers related to DMHIs in Indigenous and traditional populations in the Global South. No restrictions will be applied regarding the languages and year of publication. The search will be conducted in the following databases: MEDLINE, CINAHL, PsycINFO, Scopus, Embase, BVS Lilacs, African Index Medicus, and Index Medicus for the South-East Asia Region. Study selection and data extraction will be performed independently by three reviewers. The synthesis will include a numerical summary to provide an overview of the characteristics of the included studies, as well as a qualitative analysis aimed at identifying, analysing, and reporting recurring patterns and emerging categories within the data. The results will inform the development of future culturally competent digital mental health strategies tailored to the needs of Indigenous and traditional communities in the Global South. Protocol Tue, 16 Sep 2025 00:00:00 GMT Saidy ElianaArias-Murcia, Johanna CarolinaSánchez-Castro, RobertHrynyschyn, ChristianeStock, Tue, 16 Sep 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101161 https://www.explorationpub.com/Journals/edht/Article/10111 Not applicable. Editorial Thu, 23 Feb 2023 00:00:00 GMT Atanas G.Atanasov, Thu, 23 Feb 2023 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10111 https://www.explorationpub.com/Journals/edht/Article/101162 Medicine is undergoing a deep technological transformation, with surgery on the cusp of this change, as technologies such as artificial intelligence (AI), augmented reality (AR), real-time imaging, and robotics converge to transform operative care. These innovations are now progressively being integrated into practice, driving precision surgery closer to reality. We aimed to assess how the convergence of AI, AR, real-time imaging, and robotics is advancing precision surgery and to outline the next wave of operative care. For doing this, we conducted a narrative perspective of publications that address AI-driven decision-making, AR-guided navigation, semi-autonomous robotics, and real-time imaging tailoring in surgical contexts. We observed that AI algorithms are expanding the potential of medicine by analyzing diverse data sets to optimize treatment strategies. AR-based navigation systems overlay digital anatomical information onto the surgical field, improving surgeon awareness and accuracy. Concurrently, AI-powered robotics are beginning to perform some surgical tasks semi-autonomously, potentially shortening procedure times and improving patient outcomes. Over time, the synergy between these disciplines may yield a new era of surgery: One where patient stratification guides operative decisions and where surgeons rely on data-driven systems for intraoperative feedback. This approach reinforces the principles of precision medicine and points toward a future in which surgery evolves hand in hand to improve clinical outcomes and patient safety. The synergy of data-driven surgery and personalized therapeutics brings a new era in precision medicine in which operative decisions may be dynamically tailored to the individual, promising greater safety and better clinical results. Perspective Thu, 18 Sep 2025 00:00:00 GMT AnnaTrinidad Borràs, DiegoBenavent, Thu, 18 Sep 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101162 https://www.explorationpub.com/Journals/edht/Article/10112 Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication. Perspective Wed, 31 May 2023 00:00:00 GMT RonanLordan, Hari PrasadDevkota, Wed, 31 May 2023 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10112 https://www.explorationpub.com/Journals/edht/Article/101164 Aim: Given the ubiquitous use of social media among young adults, understanding its impact on their psychological well-being is increasingly important. Research has identified negative associations between social media use and internalizing problems, such as depression and anxiety. Building on the previous research, the current study explored the mediating role of self-esteem in the associations between social media rumination (SMR) and symptoms of depression and anxiety in college students. Additionally, the study investigated the moderating role of gender in these associations. Methods: The study sample consisted of 551 college students (mean age = 19 years; 36% men, 23.8% White) from a diverse midwestern university. The participants completed measures of depression (PHQ-9), anxiety (GAD-7), self-esteem (RSES), and SMR (Social Media Rumination Scale [SMRS]). An exploratory factor analysis was performed on the SMRS and supported a one-factor structure for the measure. Main analyses were conducted in R using PROCESS Model 4 and examined the associations between SMR and symptoms of depression and anxiety, with self-esteem as a mediator, and gender as a moderator. Additionally, time spent on social media and the number of posts per week were included as covariates in the analyses. Results: Results indicated that SMR, above and beyond time spent on social media and type of engagement, was indirectly associated with depression and anxiety through self-esteem, and gender did not moderate these associations. Conclusions: The study’s findings contribute to our understanding of the mechanisms linking social media use to internalizing problems, highlighting the crucial role of self-esteem in this process. Moreover, the study offers valuable insights for developing targeted interventions aimed at mitigating the negative effects of social media use on mental health by addressing SMR and bolstering self-esteem in young adults. Original Article Wed, 15 Oct 2025 00:00:00 GMT ShiyuanChen, RuthJeong, MorganFellows, IsabellaSibenaller, MichelleDemaray, Wed, 15 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101164 https://www.explorationpub.com/Journals/edht/Article/10113 Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology. Letter to the Editor Mon, 07 Aug 2023 00:00:00 GMT SalmanKhan, Dilip KumarKandukuri, Elakeya UdhayaSubramaniyan, ArunSundarMohanaSundaram, Mon, 07 Aug 2023 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10113 https://www.explorationpub.com/Journals/edht/Article/10114 Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent. Protocol Wed, 27 Sep 2023 00:00:00 GMT PraveenKumar, Thanh H.Nguyen, Phong V.V.Le, JinhuiYan, LeiZhao, Brian F.Allan, Andrew W.Taylor-Robinson, Wed, 27 Sep 2023 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10114 https://www.explorationpub.com/Journals/edht/Article/10115 X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X. Perspective Tue, 28 Nov 2023 00:00:00 GMT HimelMondal, Atanas G.Atanasov, FabianEibensteiner, MojcaHribersek, StefanBrandstätter, MaimaMatin, RonanLordan, MariaKletecka-Pulker, HaraldWillschke, Tue, 28 Nov 2023 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10115 https://www.explorationpub.com/Journals/edht/Article/10116 The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes. Letter to the Editor Thu, 11 Jan 2024 00:00:00 GMT Catherine L.Clelland, StuartMoss, James D.Clelland, Thu, 11 Jan 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10116 https://www.explorationpub.com/Journals/edht/Article/10117 Aim: Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions. Methods: Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools. Results: Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement. Conclusions: SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality. Original Article Wed, 28 Feb 2024 00:00:00 GMT HuijunLi, ShunwenYang, HongmeiChi, LihuaXu, TianhongZhang, FengBao, William S.Stone, JijunWang, Wed, 28 Feb 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10117 https://www.explorationpub.com/Journals/edht/Article/10118 Aim: After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures. Methods: The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation. Results: In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status. Conclusions: This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way. Original Article Wed, 28 Feb 2024 00:00:00 GMT ApostolosApostolakis, DimitrisBarmpakos, SofiaMavrikou, George MariosPapaionannou, VasileiosTsekouras, KyriakiHatziagapiou, EleniKoniari, MaroulaTritzali, AthanasiosMichos, George P.Chrousos, ChristinaKanaka-Gantenbein, GrigorisKaltsas, SpyridonKintzios, Wed, 28 Feb 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10118 https://www.explorationpub.com/Journals/edht/Article/10119 Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain. Original Article Thu, 29 Feb 2024 00:00:00 GMT AshrafeAlam, Victor R.Prybutok, Thu, 29 Feb 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/10119 https://www.explorationpub.com/Journals/edht/Article/101110 The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma (JMIR Pediatr Parent. 2019;2:e12427. doi: 10.2196/12427; Cochrane Database Syst Rev. 2018;8:CD012489. doi: 10.1002/14651858.CD012489.pub2; Int J Environ Res Public Health. 2020;17:7750. doi: 10.3390/ijerph17217750; J Med Internet Res. 2017;19:e113. doi: 10.2196/jmir.6994; J Med Internet Res. 2021;23:e25472. doi: 10.2196/25472; Ann Allergy Asthma Immunol. 2015;114:341–2.E2. doi: 10.1016/j.anai.2014.12.017; J Med Internet Res. 2022;24:e38030. doi: 10.2196/38030; Int J Qual Methods. 2021;20:16094069211008333. doi: 10.1177/16094069211008333). However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area. Review Thu, 28 Mar 2024 00:00:00 GMT MirkoCasu, PasqualeCaponnetto, Thu, 28 Mar 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101110 https://www.explorationpub.com/Journals/edht/Article/101111 As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation. Commentary Mon, 08 Apr 2024 00:00:00 GMT AndrewLarkin, MeganMacDonald, DixieJackson, Molly L.Kile, PerryHystad, Mon, 08 Apr 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101111 https://www.explorationpub.com/Journals/edht/Article/101112 Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions. Systematic Review Fri, 12 Apr 2024 00:00:00 GMT ArdHendriks, CoenHacking, HildeVerbeek, SilAarts, Fri, 12 Apr 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101112 https://www.explorationpub.com/Journals/edht/Article/101113 Aim: This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest. Methods: On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map. Results: The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19). Conclusions: The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health. Original Article Mon, 22 Apr 2024 00:00:00 GMT Andy Wai KanYeung, OlenaLitvinova, Nicola LuigiBragazzi, YousefKhader, Md. MostafizurRahman, ZafarSaid, Robert S. H.Istepanian, AnastasiosKoulaouzidis, Adeyemi OladapoAremu, James M.Flanagan, NavidRabiee, Sheikh Mohammed SharifulIslam, DeveshTewari, GaneshVenkatachalam, GiustinoOrlando, JosefNiebauer, Alexandros G.Georgakilas, Mohammad RezaSaeb, DaliborHrg, YufeiYuan, Muhammad AliImran, HuanyuCheng, Eliana B.Souto, Hari PrasadDevkota, Maurizio AngeloLeone, Jamballi G.Manjunatha, Nikolay T.Tzvetkov, MaimaMatin, OlgaAdamska, SabineVölkl-Kernstock, Fabian PeterHammerle, Farhan BinMatin, Bodrun NaherSiddiquea, DongdongWang, JivkoStoyanov, Jarosław OlavHorbańczuk, MagdalenaKoszarska, EmilParvanov, IgaBartel, ArturJóźwik, NataliaKsepka, BogumilaZima-Kulisiewicz, BjörnSchuller, GauravPandey, DavidBates, Tien YinWong, Benjamin S.Glicksberg, MaciejBanach, CyprianTomasik, SeifedineKadry, Stephen T.Wong, RonanLordan, Faisal A.Nawaz, Rajeev K.Singla, ArunSundarMohanaSundaram, HimelMondal, AyeshaJuhi, ShaikatMondal, MerisaCenanovic, AleksandraZielińska, ChristosTsagkaris, RonitaDe, Siva SaiChandragiri, RobertasDamaševičius, MugishaNsengiyumva, ArturStolarczyk, OkyazEminağa, MarcoCascella, HaraldWillschke, Atanas G.Atanasov, Mon, 22 Apr 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101113 https://www.explorationpub.com/Journals/edht/Article/101114 Background: This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluating blockchain’s suitability for healthcare data sharing applications. Methods: A tertiary review was conducted on existing systematic literature reviews concerning blockchain in the healthcare domain. Reviews that focused on data sharing were selected, and common key factors assessing blockchain’s suitability in healthcare were extracted. Results: Our review synthesized findings from 27 systematic literature reviews, which led to the development of a refined decision-making flowchart. This tool outlines criteria such as scalability, integrity/immutability, interoperability, transparency, patient involvement, cost, and public verifiability, essential for assessing the suitability of blockchain in healthcare data sharing. This flowchart was validated through multiple case studies from various healthcare domains, testing its utility in real-world scenarios. Discussion: Blockchain technology could significantly benefit healthcare data sharing, provided its application is carefully evaluated against tailored criteria for healthcare needs. The decision-making flowchart developed from this review offers a systematic approach to assist stakeholders in navigating the complexities of implementing blockchain technology in healthcare settings. Systematic Review Fri, 14 Jun 2024 00:00:00 GMT KunLi, Ashish RajendraSai, VisaraUrovi, Fri, 14 Jun 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101114 https://www.explorationpub.com/Journals/edht/Article/101115 The aim of this contribution is to analyze and discuss the perturbations of body-onboard medical devices caused by electromagnetic field radiations. This involves their control via electromagnetic compatibility analysis and their protection against such perturbations. The wearable, detachable, and embedded devices are first presented and their monitoring, control, forecasting, and stimulating functions are detailed. The interaction of these devices with field exposures comprising their wireless routines is then analyzed. The perturbations control of onboard devices is investigated through the mathematical solution of governing electromagnetic field equations and their appropriate protection strategies are deliberated. The involved investigations and analyses in the contribution are supported by a literature review. Mini Review Mon, 17 Jun 2024 00:00:00 GMT AdelRazek, Mon, 17 Jun 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101115 https://www.explorationpub.com/Journals/edht/Article/101116 Aim: The social media platform X, formerly known as Twitter, has emerged as a significant hub for healthcare-related conversations and sharing information. This study aims to investigate the impact and reach of the #physiotherapy hashtag on the X platform. Methods: We collected and analyzed tweets containing the hashtag #physiotherapy posted between September 1, 2022, and September 1, 2023. Data was retrieved from X using the Fedica analytics platform on October 26, 2023. The data were analyzed and expressed in number and percentage and categorical data were tested by chi-square test. Results: Over the course of one year, a total of 57,788 tweets were shared using #physiotherapy by 21,244 users, generating a remarkable 108,743,911 impressions. On average, there were 6 tweets posted per day (with a range from 3 to 9). Among the users, the majority (42%) had between 100 and 1000 followers, while 31.6% had fewer than 100 followers. The top three countries contributing to #physiotherapy tweets were the UK (29.9%), India (23.75%), and the USA (11.85%). An analysis of sentiment revealed that 84% of the tweets had a neutral tone, while 9% were positive and 7% were negative (P < 0.0001). Conclusions: The examination of tweets related to #physiotherapy unveiled a vibrant global dialogue, with active engagement from diverse backgrounds. Notably, contributions from the UK, India, and the USA were prominent. Original Article Mon, 24 Jun 2024 00:00:00 GMT HimelMondal, Michel-EdwarMickael, MaimaMatin, DaliborHrg, Marc A.Smith, Farhan BinMatin, JivkoStoyanov, Emil D.Parvanov, Atanas G.Atanasov, Mon, 24 Jun 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101116 https://www.explorationpub.com/Journals/edht/Article/101117 Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition. Original Article Tue, 25 Jun 2024 00:00:00 GMT Rogih RiadAndrawes, Susan JamuriaYousufzai, Susan SaharMattin, SusanElliott, CarolineBarakat, Tue, 25 Jun 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101117 https://www.explorationpub.com/Journals/edht/Article/101118 Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field. Original Article Mon, 01 Jul 2024 00:00:00 GMT NehaDeo, Faisal A.Nawaz, Cleadu Toit, TranTran, ChaitanyaMamillapalli, PiyushMathur, SandeepReddy, ShyamVisweswaran, ThangaPrabhu, KhalidMoidu, SandoshPadmanabhan, RahulKashyap, Mon, 01 Jul 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101118 https://www.explorationpub.com/Journals/edht/Article/101119 Information and communication technologies (ICTs) have transformed global connectivity, offering significant support to underserved populations and small businesses in developing nations. The integration of social media into the ICT landscape has further revolutionized communication and information sharing worldwide. However, despite its widespread adoption, the precise impact of social media on biomedical research remains uncertain. This manuscript seeks to examine the multifaceted roles of social media in healthcare, focusing on its applications in patient care, professional networking, education, organizational promotion, and public health programs. Additionally, it investigates social media’s significance in research, particularly its potential for data collection and analysis. A comprehensive literature review was undertaken to consolidate existing knowledge on social media’s utilization in healthcare and research. Various platforms, including social networking sites and academic networking sites, were assessed, along with their respective applications and consequences. Social media platforms have become essential tools in healthcare, facilitating professional networking, patient education, organizational promotion, and public health initiatives. In the realm of research, social media provides extensive opportunities for data collection, analysis, and collaboration, although challenges persist regarding privacy, data accuracy, and ethical considerations. The pervasive influence of social media in healthcare and research highlights its potential to enhance communication, engagement, and knowledge dissemination. However, careful adherence to ethical guidelines and privacy concerns is essential to maximize its benefits while minimizing risks. As social media continues to evolve, its role in shaping biomedical research and healthcare practices is anticipated to grow, necessitating ongoing exploration and adaptation by stakeholders. Review Fri, 05 Jul 2024 00:00:00 GMT Md SadiqueHussain, DeveshTewari, Fri, 05 Jul 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101119 https://www.explorationpub.com/Journals/edht/Article/101120 Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future. Perspective Tue, 09 Jul 2024 00:00:00 GMT Xin-JiaCai, Tue, 09 Jul 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101120 https://www.explorationpub.com/Journals/edht/Article/101121 Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved user experience, and expanded capacity. In addition, modern automation technologies, which were implemented in industrial control systems or industrial automation control systems, are essential for ensuring the availability of societies’ critical cyber-physical systems (CPSs) and the services they provide, such as healthcare. This narrative literature review produces information that can be applied when planning and implementing an interdisciplinary biomedical and health informatics (BMHI) master’s education focused on the challenges of digitalization in the health sector. The review results that virtual human twins (VHTs) are revolutionizing healthcare by addressing people’s complex medical problems with real-time monitoring and precision care while digital twin (DT) technology can make the hospital’s operational processes resilient and efficient. Thus, future BMHI education must address these technologies with a multidisciplinary approach, including computer science, information science, engineering, basic sciences, health sciences, socio-behavioral sciences, and ethical, legal, and policy aspects. Collected and cumulative data is essential for cognitive DTs. A prerequisite for this data is information sharing between different CPSs. Better information sharing and the development of scalable cognitive DTs and VHTs, the provision of critical services, quality, and cost-effectiveness, as well as health, safety, and resilience, will improve. Similarities between peoples’ health information exchange and information needed for ensuring the resilience of CPSs exist. Since humans are in many ways more complex than CPSs, security engineers have a lot to learn from VHTs in maintaining the resilience of CPSs. Cross-sectoral research and cooperation with different disciplines are essential for the progress of both human health and the resilience of CPSs. Along with interdisciplinary research cooperation, educational cooperation should also be intensified. Review Mon, 05 Aug 2024 00:00:00 GMT JyriRajamäki, Mon, 05 Aug 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101121 https://www.explorationpub.com/Journals/edht/Article/101122 Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. This study focuses on the analysis of voice features and machine learning models in the automatic detection of COVID-19. Methods: We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4,352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include long short-term memory (LSTM), CNN and Hidden-Unit BERT (HuBERT). Results: We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86% and the highest AUC of 0.93. Conclusions: The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art. Original Article Mon, 12 Aug 2024 00:00:00 GMT YuyangYan, WafaaAljbawi, Sami O.Simons, VisaraUrovi, Mon, 12 Aug 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101122 https://www.explorationpub.com/Journals/edht/Article/101123 Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attributes. By enhancing traditional AI models with self-aware capabilities, this approach seeks to improve diagnostic accuracy and patient outcomes in medical settings. Methods: This research discusses an introduction of SAL methodology into the medical field. SAL incorporates continuous self-assessment, allowing the AI to adjust its parameters and structure autonomously in response to changing inputs and performance metrics. The methodology is applied to medical diagnostics, utilizing real medical datasets available in the public domain. These datasets encompass a partial range of medical conditions and diagnostic scenarios, providing an initial test background for a preliminary evaluation of the effectiveness of SAL in a real-world context. Results: The study shows encouraging results in enhancing diagnostic accuracy and patient outcomes. Through continuous assessment and autonomous adjustments of its own neural network architecture, self-aware AI systems show improvements in adaptability, in the classification of real datasets and diagnostic process. Additional experiments on expanded data sets are necessary for validating these preliminary results. Conclusions: Tests on real data show that Self-Aware Deep Neural Networks present promising potential for improving medical diagnostic capabilities. They offer advantages such as enhanced adaptability to varying data qualities, improved error detection, efficient resource allocation, and increased transparency in AI-assisted diagnoses. However, considering the limited size of the test data set used in this research, further validation is required through additional experiments on larger data sets. Original Article Thu, 15 Aug 2024 00:00:00 GMT PaoloDell’Aversana, Thu, 15 Aug 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101123 https://www.explorationpub.com/Journals/edht/Article/101124 Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkably reshaping is biomedical scientific writing. Thousands of AI-based tools can be applied at every step of the writing process, improving time effectiveness, and streamlining authors’ workflow. Out of this variety, choosing the best software for a particular task may pose a challenge. While ChatGPT receives the necessary attention, other AI software should be addressed. In this review, we draw attention to a broad spectrum of AI tools to provide users with a perspective on which steps of their work can be improved. Several medical journals developed policies toward the usage of AI in writing. Even though they refer to the same technology, they differ, leaving a substantially gray area prone to abuse. To address this issue, we comprehensively discuss common ambiguities regarding AI in biomedical scientific writing, such as plagiarism, copyrights, and the obligation of reporting its implementation. In addition, this article aims to raise awareness about misconduct due to insufficient detection, lack of reporting, and unethical practices revolving around AI that might threaten unaware authors and medical society. We provide advice for authors who wish to implement AI in their daily work, emphasizing the need for transparency and the obligation together with the responsibility to maintain biomedical research credibility in the age of artificially enhanced science. Review Thu, 05 Sep 2024 00:00:00 GMT MichalFornalik, MagdalenaMakuch, AnnaLemanska, SandraMoska, MonikaWiczewska, IwonaAnderko, LauraStochaj, MartaSzczygiel, AleksandraZielińska, Thu, 05 Sep 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101124 https://www.explorationpub.com/Journals/edht/Article/101125 Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowadays allowing the acquisition of high-quality pictures. Compared to professional cameras, it has advantages and disadvantages. The latter comprise photographs out of focus, poorly framed, and lighting problems due to shadows, artifacts, and color alterations, among other problems mainly mediated by the operator. Such defects can limit the proper interpretation of the image representing the patient’s condition. This perspective aims to describe the basic concepts of photography and the functional features of SPhs. This will allow programming those devices properly for oral telemedicine (OTM), understanding their limitations, and correcting errors for the photographs to be used effectively. We also include empirical solutions and illustrations showing that photography with SPhs could be easily executable by any health professional and even by the patients themselves. Perspective Sat, 14 Sep 2024 00:00:00 GMT Eduardo D.Piemonte, Gerardo M.Gilligan, María FernandaGalindez Costa, Jerónimo P.Lazos, Sat, 14 Sep 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101125 https://www.explorationpub.com/Journals/edht/Article/101126 ChatGPT is one of the promising AI-based language models which has the potential to contribute to pharmacy settings in many aspects. This paper focuses on the possible aspects of pharmacy management where ChatGPT can contribute, the prevalence of its use in Saudi Arabia as a practical insight, case studies showing the potential of ChatGPT in answering health-related enquiries, its benefits, challenges, and future prospects of it. Helping clients, verifying medication, examining for potential reactions to drugs, identifying potential interaction between drugs, providing recommendation for suitable alternative medication therapies, assisting healthcare workers and supporting the search for novel medication are the biggest roles that are cited. The study highlights several benefits of using ChatGPT, including greater medical supervision, fewer drug errors, greater power over existing equipment, and support to study about the medicine sector. However, concerns about security, reliability, privacy, over-reliance on AI, and lack of natural judgement must be addressed by careful implementation under human review. The study also provided insight of practical application of ChatGPT in pharmacy education and possible ways of implementing ChatGPT in getting improved care and optimized operation. The future prospect of ChatGPT is promising but requires increased precision, integration of it into education programs, progressing of patient treatment and interaction, and facilitating novel research abilities. In general, the review suggests that ChatGPT has the potential to improve and modernize pharmacy processes but cautious implementation of this developing AI technology, combined with human knowledge is important to improve healthcare in the pharmaceutical field. Review Wed, 18 Sep 2024 00:00:00 GMT Abdullah AlNoman, MD Ismail AhmedFahim, Tamanna ShahrinTonny, Afroza AkterSamia, Sakib M.Moinuddin, Wed, 18 Sep 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101126 https://www.explorationpub.com/Journals/edht/Article/101127 In the United Kingdom (UK), the current prevalence rates of oropharyngeal cancer linked to human papillomavirus (HPV) are 6.29% and 2.04% in men and women, respectively. Over the years, the burden of this disease has increased in the UK, and this is mainly due to the rising prevalence of HPV infection in the UK. Research evidence has shown that over 70% of oropharyngeal cancers in the UK are linked to HPV. Oral sex is the major route of transmission of HPV, and over 63% of UK young adults are found to have a positive history of oral sex practice. However, only a minority of the UK population are aware of HPV-associated oropharyngeal cancer; this therefore calls for more public health efforts to increase awareness and knowledge on HPV-associated oropharyngeal cancer in the UK. While the use of technology-based, clinic-based, and community-based interventions have been employed to improve public awareness and knowledge on the role of HPV-associated oropharyngeal cancer, mobile health (mhealth) interventions have not been seriously explored despite existing robust evidence on the effective roles of mhealth in improving awareness and knowledge in diverse diseases. This article therefore calls for the adoption and use of mhealth interventions in educating the UK’s population on HPV-associated oropharyngeal cancer. The use of mhealth interventions in this regard is highly viable as its implementation closely aligns with the country’s National Health Service (NHS) commitment towards the digital transformation of the UK’s healthcare system. Perspective Mon, 30 Sep 2024 00:00:00 GMT Kehinde KazeemKanmodi, Afeez AbolarinwaSalami, Jacob NjidekaNwafor, Mon, 30 Sep 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101127 https://www.explorationpub.com/Journals/edht/Article/101128 Geographic areas like Argentine Patagonia face significant barriers in the fight against oral cancer due to great distances, extreme weather conditions, and a shortage of specialists. These factors contribute to delayed diagnosis and treatment, adversely affecting patient outcomes. The aim of this study was to describe a pilot project to establish the telemedicine network of Chubut (Argentine Patagonia) for the early diagnosis of oral cancer. This perspective study also aimed to describe the advantages and disadvantages of using this tool in remote areas with limited access to healthcare services. Healthcare professionals, including nurses, dentists, doctors, and healthcare workers, were trained in the early diagnosis of oral cancer and high-risk oral lesions by five specialists in Oral Medicine, who traveled throughout Argentine Patagonia. Additionally, training was provided on the use of smartphones to obtain clinical images and data for remote consultations via telemedicine with a specialized center. Over 2,000 km were traveled, and more than 100 healthcare professionals were trained in six towns and localities in Patagonia, Argentina, encountering various limitations for the use of telemedicine in remote areas, such as connectivity issues. The first telemedicine network of Patagonia for the diagnosis of oral cancer was created and is now operational, receiving teleconsultations and referrals from the professionals trained during the journey. This study highlighted that telemedicine is an important tool to overcome geographical barriers and improve access to medical care, especially in remote areas. It promotes agility and speed in referrals and optimizes the available resources of the health system. Future studies should analyze the impact of telemedicine in decreasing the delay of oral cancer diagnosis in Southern Argentina. Perspective Fri, 11 Oct 2024 00:00:00 GMT RominaAndrian, AstridMüller, Juan MartinPimentel Solá, IgnacioMolina Ávila, GerardoGilligan, Fri, 11 Oct 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101128 https://www.explorationpub.com/Journals/edht/Article/101129 Teledentistry has emerged as a promising tool in bridging the gap in healthcare accessibility, particularly in regions like Latin America region, where resources for oral healthcare are often limited. Drawing upon a comprehensive review of literature, this overview assessed the applications and clinical outcomes of teledentistry in diagnosing oral potentially malignant disorders (OPMDs) and oral cancer, highlighting the challenges and opportunities specific to the Latin American context. Moreover, it examined the integration of artificial intelligence algorithms and teledentistry for enhancing diagnostic accuracy, thereby optimizing resource allocation and improving patient outcomes. By elucidating the current landscape and future prospects, this overview provided insights for policymakers, healthcare providers, and researchers, fostering advancements in oral healthcare delivery with the aim of reducing the burden of OPMDs and oral cancer in the Latin American region. Review Wed, 23 Oct 2024 00:00:00 GMT Caique MarianoPedroso, SamanWarnakulasuriya, Alan RogerSantos-Silva, Wed, 23 Oct 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101129 https://www.explorationpub.com/Journals/edht/Article/101132 Aim: This study aims to evaluate the accuracy and readability of responses generated by two large language models (LLMs) (ChatGPT-4 and Gemini) to frequently asked questions by lay persons (the general public) about signs and symptoms, risk factors, screening, diagnosis, treatment, prevention, and survival in relation to oral cancer. Methods: The accuracy of each response given in the two LLMs was rated by four oral cancer experts, blinded to the source of the responses. The accuracy was rated as 1: complete, 2: correct but insufficient, 3: includes correct and incorrect/outdated information, and 4: completely incorrect. Frequency, mean scores for each question, and overall were calculated. Readability was analyzed using the Flesch Reading Ease and the Flesch-Kincaid Grade Level (FKGL) tests. Results: The mean accuracy scores for ChatGPT-4 responses ranged from 1.00 to 2.00, with an overall mean score of 1.50 (SD 0.36), indicating that responses were usually correct but sometimes insufficient. Gemini responses had mean scores ranging from 1.00 to 1.75, with an overall mean score of 1.20 (SD 0.27), suggesting more complete responses. The Mann-Whitney U test revealed a statistically significant difference between the models’ scores (p = 0.02), with Gemini outperforming ChatGPT-4 in terms of completeness and accuracy. ChatGPT generally produces content at a lower grade level (average FKGL: 10.3) compared to Gemini (average FKGL: 12.3) (p = 0.004). Conclusions: Gemini provides more complete and accurate responses to questions about oral cancer that lay people may seek answers to compared to ChatGPT-4, although its responses were less readable. Further improvements in model training and evaluation consistency are needed to enhance the reliability and utility of LLMs in healthcare settings. Original Article Tue, 19 Nov 2024 00:00:00 GMT MárcioDiniz-Freitas, Rosa MaríaLópez-Pintor, Alan RogerSantos-Silva, SamanWarnakulasuriya, PedroDiz-Dios, Tue, 19 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101132 https://www.explorationpub.com/Journals/edht/Article/101133 This research provided an in-depth analysis of endoscopic capsules as an innovative application of the Internet of Things (IoT) in healthcare. The study revealed the importance of these systems in advancing gastrointestinal diagnostics due to their non-invasive nature and ability to provide comprehensive internal imaging. The work systematically investigated the device’s technical design, power management strategies, communication protocols, and how it performs its secure and efficient operations. Findings from this analysis highlighted the transformative impact of these capsules despite current constraints, such as battery limitations and procedural costs. Ultimately, this wide review confirmed that endoscopic capsules redefine medical diagnostics, fusing patient comfort with innovative technology. Moreover, as developments continue, these devices have promising potential to shape the future of intelligent, interconnected healthcare solutions. Review Mon, 25 Nov 2024 00:00:00 GMT Vasile DenisManolescu, HamzahAlZu’bi, Emanuele LindoSecco, Mon, 25 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101133 https://www.explorationpub.com/Journals/edht/Article/101134 Somalia’s healthcare system faces significant challenges, including limited infrastructure, a shortage of healthcare professionals (2.5 physicians per 10,000 people), and geographic disparities in access to care, leading to only 35% of the population having access to basic health services. Despite these, Somalia is embracing digital health technologies to address these challenges and to improve healthcare delivery. Telehealth platforms such as Baano and SomDoctor provide remote consultations and specialized care to overcome geographical barriers. mHealth solutions, including Hello! Caafi, leverages Somalia’s expanding telecommunications network to deliver healthcare information and services. The development of an electronic immunization registry demonstrated the role of digital health records in streamlining health services and improving data accuracy. Despite the potential benefits, challenges persist, including limited and unreliable Internet access (27.6% penetration rate), and the need to ensure data privacy and security. Capacity building and digital literacy enhancement among healthcare providers and populations are crucial. Learning from successful digital health initiatives in African countries that have effectively used digital health technologies for medical supply delivery and for improved healthcare access is essential. The roadmap for Somalia emphasizes government leadership, public-private partnerships, context-specific solutions, and investment in digital infrastructure, capacity building, and data privacy measures. This perspective explores current digital health innovations in Somalia and their potential impact on healthcare access and quality, outlining a roadmap for establishing a sustainable digital health ecosystem. Perspective Mon, 25 Nov 2024 00:00:00 GMT Mohamed MustafAhmed, Mon, 25 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101134 https://www.explorationpub.com/Journals/edht/Article/101135 The health sector in Yemen has experienced significant challenges due to prolonged conflict and suboptimal governance, making the development of digital health (DH) crucial. This study highlights the urgent need for the strategic implementation of health interventions in a country where fully functional healthcare facilities, low-income levels, damaged infrastructure, and suboptimal governance limit the effectiveness of traditional interventions. It discusses the prioritized step for advancing DH as a root issue that needs to be addressed first and highlights the importance of effective and efficient management of available resources. The development of telecommunication infrastructure is a fundamental pillar for advancing DH in the country. This comes along with consideration of effective management of the available resources and collaborative efforts among all parties, which are critically important to remove restrictions and constraints relevant to the administrative division and fragmentation of the healthcare system and objectively ensure universal coverage of telecommunications and healthcare services nationwide. By leveraging DH technologies (DHTs), Yemen can overcome these obstacles and revolutionize healthcare delivery. Implementing DHTs and related projects can ensure equitable access to high-quality healthcare services, particularly for impoverished individuals. However, the success of these initiatives relies on a well-established supportive policy and regulatory framework, improved public communication systems, targeted strategies, community engagement, and collaboration between medical service providers and community healthcare workers. Awareness campaigns, workshops, research collaborations, and engagement with international organizations are highly recommended to address challenges and foster the growth and development of DH in Yemen. Perspective Mon, 25 Nov 2024 00:00:00 GMT Omar Abdulkarim SaeedAlhammadi, Hajer IbrahimMohamed, Shuaibu SaiduMusa, Mohamed MustafAhmed, Misha AbaynehLemma, UwamahoroJoselyne, BananezaRoméo, YinusaAbdullahi, Zhinya KawaOthman, Mohammed RaihanatuHamid, OmarKasimieh, Don EliseoLucero-Prisno III, SafouaneLabyad, Olalekan JohnOkesanya, Mon, 25 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101135 https://www.explorationpub.com/Journals/edht/Article/101136 Aim: The primary aim was to develop and test a telemedicine program for oral cancer screening by dentists in primary care. The secondary aim was to analyze the sensitivity of the provisional diagnosis compared to the definitive diagnosis. Methods: A retrospective observational study that used telemedicine for oral cancer case detection was conducted in Cordoba, Argentina from 2018 to 2023, oral medicine specialists provided in-person training for dentists on the clinical recognition and early diagnosis of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD), and telemedicine use for the early detection of oral cancer. The trained professionals conducted opportunistic screenings in their workplaces. When encountering a suspicious lesion on the oral mucosa, they collected relevant patient data and clinical photographs of the lesion, sharing these with the reference center. The specialized center was based at the Oral Medicine unit at the Facultad de Odontología, Universidad Nacional de Córdoba, Argentina. The specialists suggested radiographic examinations and/or pre-surgical laboratory tests and, if necessary, expedited referral to the specialized center for in-person assessment and definitive diagnosis. Results: Cases with clinical suspicion of OSCC and OPMD were referred to the reference center. In all cases, the definitive diagnosis was obtained within less than 1 month. Eleven out of 12 cases of OSCC were diagnosed within 2 weeks, with only 1 case diagnosed at 1 month due to some patient delay. The concordance between the clinical suspicion at the time of teleconsultation and the definitive diagnosis of OSCC by the specialists was absolute (Kappa test, coefficient 1), with a sensitivity and specificity of 100%. Conclusions: Integrating telemedicine with other preventive strategies and timely referral to oral medicine specialists could potentially decrease diagnostic delays in OSCC and OPMD. Original Article Fri, 10 Jan 2025 00:00:00 GMT GerardoGilligan, RenéPanico, María FernandaGalindez Costa, JerónimoLazos, Juan CruzRomero Panico, EduardoPiemonte, Fri, 10 Jan 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101136 https://www.explorationpub.com/Journals/edht/Article/101137 Aim: Physical activity of nursing home residents can be assessed with tools such as questionnaires and standardized fitness tests. For residents with dementia, however, those tools can be cognitively challenging and difficult to administer. Consumer wearables could potentially aid as an affordable tool for ubiquitous assessment. Methods: In this pilot study with 16 participants, we explored how measurements with an off-the-shelf wearable relate to structured observations of physical activity. We collected both processed and raw tri-axial accelerometer data from Samsung wrist-worn fitness trackers. To anchor those data in the free-living environment, we compared the measurements with the physical activity scale of the Medlo behavioral observation scheme. Results: We showed that consumer wearables are a valid tool for long-term data collection in this vulnerable patient population. Conclusions: Regarding the movement intensity, the data collected by fitness trackers is overall in accordance with the data collected with the observational tool. Regarding the type of movement, we concluded that the automatic activity classification on the wearables is not yet ready for use with a mostly sedentary patient population. Original Article Thu, 16 Jan 2025 00:00:00 GMT DanielaGawehns, SuzannePortegijs, Adriana Petronella Annavan Beek, Matthijsvan Leeuwen, Thu, 16 Jan 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101137 https://www.explorationpub.com/Journals/edht/Article/101138 Confocal laser endomicroscopy (CLE) enables real-time diagnosis of oral cancer and potentially malignant disorders by in vivo microscopic tissue examination. One impediment to the widespread clinical adoption of this technology is the need for operator expertise in image interpretation. Here we review the application of AI to automatic tissue classification of CLE images and discuss the opportunities for integrating this technology to advance the adoption of real-time digital pathology thus improving speed, precision and reproducibility. Perspective Wed, 22 Jan 2025 00:00:00 GMT Simon A.Fox, Camile S.Farah, Wed, 22 Jan 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101138 https://www.explorationpub.com/Journals/edht/Article/101140 Cholera continues to pose a significant public health challenge in Nigeria, driven by poor sanitation, inadequate water quality, and climatic factors that create favorable conditions for outbreaks. Since the first epidemic in 1972, Nigeria has experienced recurrent outbreaks, with the most severe in 1991, resulting in over 7,000 deaths. Current surveillance systems and diagnostic methods are limited by infrastructural gaps, insufficient skilled personnel, and inadequate reporting, leading to delays in outbreak detection and response. These limitations exacerbate the public health burden, increasing mortality and the economic impact of cholera epidemics. This paper explores the potential of artificial intelligence (AI) and machine learning (ML) to address these challenges. AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. Case studies from other cholera-endemic regions, where AI achieved high predictive accuracy, demonstrate its transformative potential. By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. However, challenges such as data quality, standardization, and infrastructural deficits must be addressed. Multi-sectoral collaboration involving public health authorities, AI specialists, and policymakers is essential for the successful deployment of these technologies. This article concludes that AI-powered cholera surveillance systems have the potential to revolutionize public health outcomes, reducing cholera-related morbidity and mortality in resource-limited settings like Nigeria. Letter to the Editor Mon, 17 Feb 2025 00:00:00 GMT Adamu MuhammadIbrahim, Mohamed MustafAhmed, Shuaibu SaiduMusa, Usman AbubakarHaruna, Mohammed RaihanatuHamid, Olalekan JohnOkesanya, Aishat MuhammadSaleh, Don ElisoLucero-Prisno, Mon, 17 Feb 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101140 https://www.explorationpub.com/Journals/edht/Article/101130 Aim: In lung cancer research, AI has been trained to read chest radiographs, which has led to improved health outcomes. However, the use of AI in healthcare settings is not without its own set of drawbacks, with bias being primary among them. This study seeks to investigate AI bias in diagnosing and treating lung cancer patients. The research objectives of this study are threefold: 1) To determine which features of patient datasets are most susceptible to AI bias; 2) to then measure the extent of such bias; and 3) from the findings generated, offer recommendations for overcoming the pitfalls of AI in lung cancer therapy for the delivery of more accurate and equitable healthcare. Methods: We created a synthetic database consisting of 50 lung cancer patients using a large language model (LLM). We then used a logistic regression model to detect bias in AI-informed treatment plans. Results: The empirical results from our synthetic patient data illustrate AI bias along the lines of (1) patient demographics (specifically, age) and (2) disease classification/histology. As it concerns patient age, the model exhibited an accuracy rate of 82.7% for patients < 60 years compared to 85.7% for patients ≥ 60 years. Regarding disease type, the model was less adept in identifying treatment categories for adenocarcinoma (accuracy rate: 83.7%) than it was in predicting treatment categories for squamous cell carcinoma (accuracy rate: 92.3%). Conclusions: We address the implications of such results in terms of how they may exacerbate existing health disparities for certain patient populations. We conclude by outlining several strategies for addressing AI bias, including generating a more robust training dataset, developing software tools to detect bias, making the model’s code open access and soliciting user feedback, inviting oversight from an ethics review board, and augmenting patient datasets by synthesizing the underrepresented data. Original Article Wed, 13 Nov 2024 00:00:00 GMT KaiDing, ShelbyForbes, FangfangMa, GanxiLuo, JiayouZhou, YianQi, Wed, 13 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101130 https://www.explorationpub.com/Journals/edht/Article/101131 Background: Social media has become ubiquitous; its uses reach beyond connecting individuals or organizations. Many biomedical researchers have found social media to be a useful tool in recruiting patients for clinical studies, crowdsourcing for cross-sectional studies, and even as a method of intervention. Social media usefulness in biomedical research has largely been in population health and non-surgical specialties, however, its usefulness in surgical specialties should not be overlooked. Specifically in plastic surgery, social media use to understand patient perceptions, identify populations, and provide care has become an important part of clinical practice. Methods: A scoping review was performed utilizing PubMed and Medline databases, and articles were screened for the use of social media as a method of recruitment to a clinical trial, as crowdsourcing (i.e., recruitment for a cross-sectional or survey-based study), or as a method of intervention. Results: A total of 28 studies were included, which focused on majority females between 18–34 years old. Despite the ability of the internet and social media to connect people worldwide, nearly all the studies focused on the researchers’ home countries. The studies largely focused on social media’s effect on self-esteem and acceptance of cosmetic surgery, but other notable trends were analyses of patient perceptions of a disease, or surgical outcomes as reported in social media posts. Discussion: Overall, social media can be a useful tool for plastic surgeons looking to recruit patients for a survey-based study or crowdsourcing of information. Systematic Review Fri, 15 Nov 2024 00:00:00 GMT AmandaBeneat, BorisJoutovsky, VictorMoon, ArmenKasabian, AlishaOropallo, Fri, 15 Nov 2024 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101131 https://www.explorationpub.com/Journals/edht/Article/101139 This study aims to assess a new mobile application (app)’s efficacy in raising oral cancer awareness among older adults through educational videos and serious games. The app, named TEGO® (Tele-platform of Geriatric and Dental Specialties), with a video about oral cancer prevention, oral-self-examination, and serious gaming elements, like trivia and word search puzzles to reinforce the acquired knowledge was developed. Fifty-six patients, aged 60 to 80 years, were randomly selected from the Dental Clinic of the University of Chile and invited to use the app on their personal smartphones. Knowledge and attitudes were evaluated before two and four weeks after use. Oral self-examination practices were measured with a checkup guideline. The participation rate was 41.1%, mostly male (52.2%). Before using the app, 30.4% of the participants reported awareness of oral cancer, and none had performed oral self-examinations. Following two weeks after use, there was notable engagement, with 100% of participants utilizing it and responding that they had heard about oral cancer, and 56.5% having practiced an oral self-examination. This last outcome increased to 82.6% in the fourth week. The use of mHealth technologies has the potential as an effective educational tool for disseminating knowledge about oral cancer among older adults. Short Communication Thu, 23 Jan 2025 00:00:00 GMT Constanza B.Morales-Gómez, Iris L.Espinoza-Santander, LeonardoLópez-Neira, Alfredo H.von Marttens, Marjorie C.Borgeat, Ximena M.Lee, Marco A.Cornejo-Ovalle, Jorge A.Gamonal, Andrea C.Paula-Lima, Rodrigo A.Giacaman, SorayaLeón, VíctorBeltrán, Thu, 23 Jan 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101139 https://www.explorationpub.com/Journals/edht/Article/101141 Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and can lead to severe complications such as stroke. Artificial intelligence (AI) has emerged as a vital tool in predicting and detecting AF, with machine learning (ML) models trained on electrocardiogram (ECG) data now capable of identifying high-risk patients or predicting the imminent onset of AF. Precision medicine aims to tailor medical interventions for specific sub-populations of patients who are most likely to benefit, utilizing large genomic datasets. Genetic studies have identified numerous loci associated with AF, yet translating this knowledge into clinical practice remains challenging. This paper explores the potential of AI in precision medicine for AF and examines its advantages, particularly when integrated with or compared to genomics. AI-driven ECG analysis provides a practical and cost-effective method for early detection and personalized treatment, complementing genomic approaches. AI-based diagnosis of AF allows for near-certain prediction, effectively relieving cardiologists of this task. In the context of preventive identification, AI enhances the accuracy of predictive models from 75% to 85% when ML is employed. In predicting the exact onset of AF—where human capability is virtually nonexistent—AI achieves a 74% accuracy rate, offering significant added value. The primary advantage of utilizing ECGs over genomic data lies in their ability to capture lifetime variations in a patient’s cardiac activity. AI-driven analysis of ECGs enables dynamic risk assessment and personalized adaptation of therapeutic strategies, optimizing patient outcomes. Genomics, on the other hand, enables the personalization of care for each patient. By integrating AI with ECG and genomic data, truly individualized care becomes achievable, surpassing the limitations of the “average patient” model. Perspective Tue, 25 Feb 2025 00:00:00 GMT Jean-MarieGrégoire, CédricGilon, FrançoisMarelli, HuguesBersini, StéphaneCarlier, Tue, 25 Feb 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101141 https://www.explorationpub.com/Journals/edht/Article/101142 Aim: Genetic instability represents the hallmark of carcinogenesis. For cancer, the retinoblastoma (RB) gene defect allowing genetic instability was successfully exploited to eliminate cancer. Similarly, this study aims to assess the genetic instability of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein’s S1/S2 furin cleavage site in hopes of applying oligonucleotide-based therapeutics to suppress infectivity by exploiting hypermutability. Methods: The Basic Local Alignment Search Tool was used to search for homology. Protein or nucleotide sequences were obtained from the National Center for Biotechnology Information database. BioEdit was used for multiple sequence alignment. Python-enhanced molecular graphics program was used for molecular modeling. Results: To assess feasibility, comparative sequence alignment was performed on S1/S2 site plus juxtaposing residues of SARS-CoV-2 and avian infectious bronchitis virus (IBV) isolate AL/7052/97 that belongs to distinct genus. IBV amino acids correlating to 678-TNSPRRARSVASQS of SARS-CoV-2 spike protein were deciphered (nine identical, two conserved, two displaced, and one unconserved). The encoding nucleotides exhibited 14 identities, three transitions (C>U or U>C, two; G>A or A>G, one), and 15 transversions (U>A or A>U, eight; C>G or G>C, six; G>U or U>G, one) with mostly complementary base (14/15) for transversion. Analysis of SARS-CoV-2 variants corroborates that S1/S2 site continues to evolve. The overall data portrays an evolutionarily dynamic nature of S1/S2 site. The potential role of intragenomic ‘microhomology-mediated template switching’ by RNA-dependent RNA polymerase is described. Conclusions: To apply virolytic pressure, peptide-guided oligonucleotides targeting S1/S2 site-encoding sequences may be deployed to trigger genomic RNA degradation. A potential consequence is that resistant variants (if emerge) may carry mutation(s) in S1/S2 site-encoding sequence to abrogate hybridization, which (by default) may encode defective substrate for furin. Thus, through ‘targeting oligonucleotides directed devolution’ of S1/S2 site, the infectivity of SARS-CoV-2 may be attenuated. An alternative strategy of oligonucleotide-based therapeutic editing by adenosine deaminases acting on RNA (ADAR) is mentioned. Original Article Thu, 20 Mar 2025 00:00:00 GMT Frank-UnHong, Miguel MarcianoCastro, Klaus D.Linse, Thu, 20 Mar 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101142 https://www.explorationpub.com/Journals/edht/Article/101143 Mental healthcare in a range of countries faces challenges, including rapidly increasing demand at a time of restricted access to services, insufficient mental healthcare professionals and limited funding. This can result in long delays and late diagnosis. The use of artificial intelligence (AI) technology to help to address these shortcomings is therefore being explored in a range of countries, including the UK. The recent increase in reported studies provides an opportunity to review the potential, benefits and drawbacks of this technology. Studies have included AI-based chatbots for patients with depression and anxiety symptoms; AI-facilitated approaches, including virtual reality applications in anxiety disorders; avatar therapy for patients with psychosis; AI humanoid robot-enhanced therapy, for both children and the isolated elderly in care settings; AI animal-like robots to help patients with dementia; and digital game interventions for young people with mental health conditions. Overall, the studies showed positive effects and none reported any adverse side effects. However, the quality of the data was low, mainly due to a lack of studies, a high risk of bias and the heterogeneity of the studies. Importantly also, longer-term effects were often not evident. This suggests that translating small-scale, short-term trials into effective large-scale, longer-term real-world applications may be a particular challenge. While the use of AI in mental healthcare appears to have potential, its use also raises important ethical and privacy concerns, potential risk of bias, and the risk of unintended consequences such as over-diagnosis or unnecessary treatment of normal emotional experiences. More robust, longer-term research with larger patient populations, and clear regulatory frameworks and ethical guidelines to ensure that patients’ rights, privacy and well-being are protected, are therefore needed. Mini Review Thu, 03 Apr 2025 00:00:00 GMT MichaelBaber, BarbaraBaker, Thu, 03 Apr 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101143 https://www.explorationpub.com/Journals/edht/Article/101144 Artificial intelligence (AI) increasingly influences biomedical scientific writing and clinical practice. The recent article by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024) explores AI’s capabilities, challenges, and ethical considerations in scientific communication, particularly highlighting tools like ChatGPT and Penelope.ai. This commentary aims to reflect on and expand the key themes presented by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024), emphasizing AI’s role in auditory healthcare, particularly in otolaryngology and auditory rehabilitation. The discussion is based on a critical review and synthesis of recent literature on AI applications in scientific writing and auditory healthcare. Key technologies such as generative AI platforms, machine learning algorithms, and mobile-based auditory training systems are highlighted. AI has shown promising results in enhancing manuscript preparation, literature synthesis, and peer review workflows. In clinical practice, adaptive AI models have improved cochlear implant programming, leading to up to 30% gains in speech perception accuracy. Mobile apps and telehealth platforms using AI have also improved listening effort, communication confidence, and access to care in remote settings. However, limitations include data privacy concerns, lack of population diversity in datasets, and the need for clinician oversight. AI presents transformative opportunities across biomedical science and healthcare. To ensure its responsible use, interdisciplinary collaboration among clinicians, researchers, ethicists, and technologists is essential. Such collaboration can help develop ethical frameworks that enhance innovation while safeguarding patient well-being and scientific integrity. Letter to the Editor Tue, 08 Apr 2025 00:00:00 GMT AynurAliyeva, Tue, 08 Apr 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101144 https://www.explorationpub.com/Journals/edht/Article/101145 Background: The use of artificial intelligence (AI) has been shown to enhance human life quality by making it easier, safer, and more efficient. However, there is currently limited evidence about the applicability of AI in health insurance and easing the complexity of insurance operations. This study seeks to systematically review the literature related to the application, challenges, and opportunities of applying AI in the healthcare insurance industry. Methods: A systematic review approach was utilized, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The method included an exploratory and narrative design, a two-phase search strategy, eligibility criteria, and analysis. Results: The search yielded 520 eligible articles. Twelve articles were eligible, evaluated, and analyzed in this study. Most articles discussed AI’s use in healthcare insurance to detect fraud, improve underwriting accuracy and transparency, and resolve medical information asymmetry. For claim processes, virtual agents, chatbots, customer engagement, telematics, and underwriting, algorithms were essential. However, technical skill is needed to create and deploy AI systems, and privacy was an issue due to massive data and algorithms that could abuse user data. Discussion: The implementation of AI encounters various challenges, such as insufficient knowledge among users, a deficit in technical expertise and support, shortcomings in data strategy, and a growing reluctance towards AI. Privacy presents a challenge in AI, especially because of the widespread use of large data sets and algorithms that could misuse consumer information. Systematic Review Tue, 22 Apr 2025 00:00:00 GMT Ahmed AliAlkhelb, SalahAlshagrawi, Tue, 22 Apr 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101145 https://www.explorationpub.com/Journals/edht/Article/101146 Aim: This study aimed to understand the mediating and moderating effects of self-esteem’s relationship with gaming disorder (GD). Methods: Participants (N = 1,712) were recruited from online gaming forums. A battery of measures including GD, self-esteem, depression, anxiety, escapism, and playing time were completed. Results: Escapism, depression, and playing time have a significant mediating effect on self-esteem’s relationship with GD. Escapism and depression explained most of the mediated effect, with playing time showing a much smaller effect. Anxiety was not a significant mediator. Unexpectedly, high self-esteem does not appear to buffer against the effects escapism and playing time have on GD. This contradicts clinical literature that promotes high self-esteem as a resilience factor. Conclusions: Mediating effects of self-esteem’s relationship with GD were identified in this study. Moderators other than self-esteem might be more prudent to investigate in GD research. Original Article Wed, 30 Apr 2025 00:00:00 GMT MichaelKavanagh, Wed, 30 Apr 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101146 https://www.explorationpub.com/Journals/edht/Article/101147 This narrative review aims to appraise the evidence on artificial intelligence models for early diagnosis and risk stratification of oral cancer, focusing on data modalities, methodology differences, applications in the diagnostic flow and models’ performance. Models for early diagnosis and screening provide non-invasive diagnosis without the need for specialized instruments, which is ideal for early detection as a low-cost system. Supervised learning with well-annotated data provides reliable references for training the models, and therefore, reliable and promising results. Risk prediction models can be built based on medical record data, demographic data, clinical/histopathological descriptors, highly standardized images or a combination of these. Insights on which patients have a greater chance of malignancy development or disease recurrence can aid in providing personalized care, which can improve the patient’s prognosis. Artificial intelligence models demonstrate promising results in early diagnosis and risk stratification of oral cancer. Review Fri, 09 May 2025 00:00:00 GMT Anna Luíza DamacenoAraújo, Caique MarianoPedroso, Pablo AgustinVargas, Marcio AjudarteLopes, Alan RogerSantos-Silva, Fri, 09 May 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101147 https://www.explorationpub.com/Journals/edht/Article/101148 In recent years, patient engagement has emerged as a cornerstone in clinical decision-making, medical research, and health policy development, with its multifaceted value widely recognized by stakeholders across the healthcare continuum. However, digital health technologies, which are designed to enhance patient engagement, often fall short of their full potential due to developers’ limited understanding of patients’ needs and preferences. This perspective paper argues for adopting a patient-centered approach, emphasizing the critical importance of developers immersing themselves in patient communities to gain richer insights into patients’ lived experiences. Such an approach can lead to improved usability of digital health tools, enhanced user experience, and increased patient motivation, ultimately fostering more effective patient engagement in medical practice. Although challenges persist in the effective collection, analysis, and implementation of user feedback, prioritizing patient engagement remains crucial for optimizing health outcomes and enhancing the overall patient experience. By embracing this approach, developers can bridge the gap between technological innovation and patient needs, promoting more meaningful interactions and ultimately contributing to the advancement of healthcare systems and improved population health. Perspective Tue, 13 May 2025 00:00:00 GMT HongnanYe, Tue, 13 May 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101148 https://www.explorationpub.com/Journals/edht/Article/101149 Background: The main theme of research literature on burnout has yet to be investigated. Aims: This bibliometric study evaluated the research literature on burnout and health, indexed in Web of Science (WoS), to reveal its expansion and the most prolific authors, institutions, countries, journals, and journal categories. The recurring themes of the literature were also identified. Methods: In December 2023, the WoS Core Collection database was queried with: TS = [(“burnout*” OR “burn out*” OR “burn-out*”) AND (“health*” OR “illness*” OR “disease*” OR “well-being*” OR “wellbeing*”)]. The search yielded publications with these words presented in their title, abstract, or keywords. No filter was placed to restrict the search. Publication and citation counts were recorded directly from the database, whereas subsequent analyses were performed with VOSviewer. Results: The search yielded 26,492 publications. The literature has been growing steadily in the 2000s and more quickly in the 2010s. Nearly one-third of the publications had contributions from the United States. The most prolific journals involved some open-access mega-journals and journals from psychology, medicine, and nursing. Depression and anxiety associated with burnout were recurring themes in the literature. The research community has been explaining burnout by the highly cited Job Demands-Resources (JD-R) model. Discussion: This work demonstrated the usefulness of a bibliometric analysis to identify key stakeholders and major themes of burnout research. Systematic Review Wed, 28 May 2025 00:00:00 GMT Andy Wai KanYeung, OlenaLitvinova, MaimaMatin, Michel-EdwarMickael, MariaKletecka-Pulker, Atanas G.Atanasov, HaraldWillschke, ThomasWochele-Thoma, Wed, 28 May 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101149 https://www.explorationpub.com/Journals/edht/Article/101150 The enormous global burden of cancer has created the need to develop cutting-edge strategies for enhancing public education on cancer. Over the years, conventional educational strategies, such as the use of posters and leaflets, have been preferentially employed as public education strategies on oral cancer; however, the use of digital education-based strategies has been largely underutilized. Notably, the use of digital education-based strategies, particularly serious games, has proven to be a superior cancer education strategy, when compared to conventional strategies, due to their rigorous design and features. This commentary discusses serious games as a game changer in cancer education, itemizing their diverse roles in cancer prevention, advocacy, and management. Also, this commentary also detailed those factors that might limit the use and availability of serious games in resource-limited settings. Perspective Wed, 04 Jun 2025 00:00:00 GMT Afeez ASalami, Timothy OAladelusi, Vasthare PRamprasad, Ruwan DJayasinghe, Kehinde KKanmodi, Wed, 04 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101150 https://www.explorationpub.com/Journals/edht/Article/101151 Spatial memory, a fundamental cognitive function, enables individuals to encode, store, and retrieve information about their surroundings. Traditional assessment methods, such as paper-based tests and laboratory paradigms, often lack ecological validity and fail to capture the complexities of real-world navigation. Recent advancements in digital technologies, particularly virtual reality (VR) and mixed reality (MR), have introduced innovative tools for more immersive and accurate spatial memory assessments. VR provides controlled, replicable environments that simulate real-world navigation, while MR enhances engagement by blending virtual elements with physical spaces. This narrative review explores the cognitive mechanisms underlying spatial memory, highlighting the roles of egocentric and allocentric reference frames, as well as the neural substrates involved. The review also examines key factors influencing spatial memory performance, such as age, sex, neurological and neurodegenerative diseases. Digital tools such as the virtual Morris water maze and the VR Supermarket Test have been shown to possess enhanced ecological validity and diagnostic potential, particularly in the context of detecting early cognitive decline in Alzheimer’s disease. However, the field confronts several challenges, including the necessity for standardized protocols, the potential for adverse effects such as cybersickness, and the substantial cost associated with VR and MR systems. Future research directions in this field should include the integration of artificial intelligence for personalized assessments, and the combination of VR and MR tasks with neurophysiological techniques to advance understanding of spatial memory. Standardization, accessibility, and the creation of adaptive assessment for clinical populations will be crucial for optimizing the use of digital technologies in spatial memory research. Review Thu, 05 Jun 2025 00:00:00 GMT SaraGarcia-Navarra, LuciaSolares, MartaMendez, Thu, 05 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101151 https://www.explorationpub.com/Journals/edht/Article/101152 Aim: To explore patient satisfaction with telemedicine and its associated factors at the Rheumatology Outpatient Clinic, San Fernando Teaching Hospital (SFTH), and to determine patient preference for health-related consultations. Methods: 305 patients were surveyed via consecutive sampling. Data was obtained via interviewer-administered questionnaires in a clinical setting, capturing demographics, challenges with face-to-face consultations, and patient perspectives on telemedicine. Items from the Telemedicine Satisfaction Questionnaire and Telehealth Usability Questionnaire were modified to capture impact. Data was analyzed using descriptive and inferential statistics (SPSS version 29). Results: Most respondents were ≥ 40 years old (77.7%), Indo-Caribbean (66.2%), female (89.2%), unemployed (64.9%), and had secondary level education or higher (76.1%). Time off issues (13.0%), timing inconvenience (12.4%), and traveling costs (12.4%) were identified as challenges with face-to-face consultations. Fear of interaction (22.9%) and financial difficulty (22.7%), widely resulting from COVID-19, were additional challenges. Most patients reported satisfaction with telemedicine (71.5%), relating to easier access to health services (65.9%). Combined telemedicine and face-to-face consultations, as appropriate, were the most preferred option (73.4%). Several socio-demographic factors influenced patient satisfaction and preference for telemedicine services, with telemedicine convenience being the most significant factor. Conclusions: The results conclude that patients at the Rheumatology Outpatient Clinic are satisfied with the current telemedicine service as a method of providing continuity of care (p < 0.001). Challenges encountered with face-to-face consultations and the COVID-19 pandemic can influence patients’ level of satisfaction with and preference for telemedicine. Telemedicine convenience was the most significant factor influencing patient satisfaction and preference (p < 0.001). Most patients’ preference for a combination approach of both telemedicine and face-to-face consultations reflects the current standard of care. The findings of this study suggest that telemedicine is reasonable to incorporate into outpatient care for patients with chronic rheumatological diseases. Original Article Mon, 09 Jun 2025 00:00:00 GMT EstherRamlakhan, HaramnauthDyaanand, GavaskarRamnanansingh, Mon, 09 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101152 https://www.explorationpub.com/Journals/edht/Article/101153 Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy. Review Wed, 11 Jun 2025 00:00:00 GMT BrandonWilkins, EmilyHartman, BlakeKelley, PranaliPachika, JoshuaBradley, JamesBradley, Wed, 11 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101153 https://www.explorationpub.com/Journals/edht/Article/101154 Aim: The spread of suicide and non-suicidal self-injury (NSSI) content on social media has raised ongoing concerns about user safety and mental health. In response, social media platforms like Twitter (now X) and Meta (i.e., Facebook and Instagram) introduced content moderation policies to mitigate harm and promote safer digital environments. This study explored immediate trends in user discourse surrounding suicide and NSSI following the enactment of Meta’s graphic self-harm imagery ban. Specifically, it examined shifts in tweet tone, content type, and underlying themes immediately before and after the policy’s implementation. Methods: A corpus of 3,846 tweets was analyzed. Within this corpus, tweets spanning 32 weeks from October 18, 2018, to May 29, 2019, were selected. These dates were chosen to encompass approximately 16 weeks before and after the enactment of the policy on February 7, 2019. Tweets were categorized according to slant, tweet category, and theme. Results: The findings revealed notable shifts in online discourse. There was a significant decrease in the proportion of tweets identified as anti-self-harm tweets and a corresponding increase in the proportion of tweets aimed at understanding self-harm, many of which were coded as personal opinions or informative content. These trends suggest that while content promoting self-harm did not increase, the tone of discourse shifted toward greater nuance and reflection. This may reflect users’ growing efforts to process, contextualize, and share perspectives on self-harm in a policy-regulated environment. Conclusions: Meta’s graphic self-harm imagery ban appeared to influence how users communicated about suicide and NSSI on Twitter, prompting more content centered on understanding and discussion. However, the findings also highlight challenges in balancing harm reduction with space for personal narratives. These insights emphasize the role of policy in shaping public discourse and the need for clear moderation strategies that distinguish harmful promotion from lived experience and peer support. Original Article Fri, 20 Jun 2025 00:00:00 GMT ElnazMoghimi, KevinKeller, SanjeefThampinathan, WilliamCipolli, Hayden P.Smith, Fri, 20 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101154 https://www.explorationpub.com/Journals/edht/Article/101155 Aim: This study aimed to evaluate virtual reality restorative environments (VRREs)’ impact on university students’ mental well-being, investigate factors affecting VRRE perception and psychological recovery, understand virtual environments’ healing mechanisms, and provide recommendations for university virtual healing spaces. Methods: Semi-structured interviews were conducted with 32 participants to develop a VRRE perception evaluation system with five core and fourteen main categories. The system was then used to assess 13 virtual natural environments. Mental recovery effects were measured among 44 university students using the Schulte test (attention), Positive and Negative Affection Scale (mood), and physiological sensors (stress). Results: VRREs demonstrated significant positive effects on participants’ psychological recovery. Different virtual environments showed varying impacts on attention, negative affect, and stress levels, while effects on positive affect were consistent across environments. Virtual extraterrestrial space environments yielded the strongest improvements in attention and stress reduction, whereas mixed forest settings were most effective in decreasing negative affects. Structural equation modeling revealed that participants’ VRRE perceptions significantly influenced psychological recovery through seven of fifteen pathways. Conclusions: VRREs represent an effective intervention for supporting university students’ mental well-being. Different virtual environments offer distinct psychological benefits, with environment perception playing a crucial role in recovery outcomes. These findings provide valuable insights for designing targeted virtual healing spaces in university settings. Original Article Thu, 26 Jun 2025 00:00:00 GMT ChengchengYin, TongyuLi, BinxiaXue, ZiruiZhao, Thu, 26 Jun 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101155 https://www.explorationpub.com/Journals/edht/Article/101156 Aim: Structured light plethysmography (SLP), is a contactless optical system developed to monitor breathing patterns by analyzing chest-wall movement. It has not been thoroughly validated against other non-invasive motion analysis systems under different breathing conditions. This study therefore aimed to evaluate the criterion-validity of the SLP compared to the respiratory inductive plethysmography (RIP) at rest and after exercise. Methods: Adults underwent two simultaneous 5-minute recordings from both devices, conducted at rest and following submaximal exercise on a cycle ergometer. Timing indices and thoracoabdominal (TA) movement parameters were examined. Measurement agreement between SLP and RIP was assessed using Bland-Altman plots at rest, after exercise, and for exercise-induced changes. Results: Fifty adults (mean age 29.3 ± 6.8 years; 30 males) participated. Α total of 3,395 and 4,295 breath cycles were analyzed at rest and post-exercise, respectively. Over 92% of differences in timing parameters under both conditions were within the 95% limits of agreement (LOA) and their mean differences were found close to zero across a wide range of breath cycle magnitudes (rest: 2.62–8.06 s; post-exercise: 2.16–6.16 s). For ΤΑ movement parameters, the mean bias between devices at rest was 0.31 for ribcage amplitude (RCampi) and 0.23 for abdominal amplitude (ABampi), with LOA ranging from −0.06 to 0.66 and −0.06 to 0.52, respectively. A trend towards greater discrepancies for the individual measurements of RCampi and ABampi at higher magnitudes of TA movements was noted, especially post-exercise. A good average agreement between the devices was found for RCampi/ABampi both at rest [mean difference: 0.03, standard deviation (SD): 0.21] and after exercise (mean difference: 1.10, SD: 0.24). Conclusions: The SLP is an accurate method to quantify and measure timing indices and the ratio of the ribcage motion to the abdominal motion under different breathing conditions. Original Article Mon, 07 Jul 2025 00:00:00 GMT PanagiotisSakkatos, Mon, 07 Jul 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101156 https://www.explorationpub.com/Journals/edht/Article/101157 Aim: Oxidative stress (OS) remains an intensively studied scientific problem. The quantitative measurement of OS is an unsolved task, largely due to the existence of numerous complex, non-linear interactions of its components, which can not be measured by traditional statistical methods. Modern mathematical processing based on artificial intelligence (AI) could be a promising method of OS assessment in medicine. The aim of the study was to investigate the potential possibilities of using multilayer neural networks to improve the diagnostic informativeness of the OS indicator—antioxidant (AO) activity (AOA) in patients with cardiovascular diseases (CVDs). Methods: A cross-sectional study of a sample of 856 people, healthy volunteers and several groups of patients with CVDs (hypertension, including those complicated by coronary heart disease and/or cerebral ischemia, chronic cerebral ischemia), was carried out. The potentiometric method of determining the OS indicator, index of blood serum AOA, was used in comparison with a number of laboratory tests and clinical data. After the results of linear statistical evaluations were not satisfactory enough, а multilayer perceptron classifier was constructed for data analysis. Results: By training a neural network, it was possible to assign a patient to one of the above-mentioned groups with 85% accuracy on the basis of 8 parameters selected from all the patients’ clinical and laboratory data, including the AOA value. Conclusions: The use of multilayer neural networks can improve the diagnostic value of information obtained during the measurement of AOA index, in combination with simple laboratory tests in patients with CVDs. The application of AI algorithms is a promising tool to improve the laboratory measurement of OS and a potential solution to overcome the contradictions in the existing approaches to the evaluation of OS. Original Article Tue, 15 Jul 2025 00:00:00 GMT YanKazakov, AlexanderHalperin, KhienaBrainina, Tue, 15 Jul 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101157 https://www.explorationpub.com/Journals/edht/Article/101158 Aim: Colorectal cancer is a leading cause of cancer-related mortality, emphasising the need for accurate polyp segmentation during colonoscopy for early detection. Existing methods often struggle to generalize effectively across diverse clinical scenarios. This study introduces DeepPolyp, an artificial intelligence framework designed for comprehensive benchmarking and real-time clinical deployment of polyp segmentation models. Methods: Transformer-based segmentation models, SegFormer and SSFormer, were trained from scratch using an extensive dataset comprising public collections (CVC-ClinicDB, ETIS-LaribPolypDB, Kvasir) and recently augmented datasets (PolypDataset-TCNoEndo, PolypGen). Training involved standardized data augmentation, learning rate schedules, and early stopping. Models were evaluated using Dice and Intersection over Union (IoU) metrics. Real-time inference performance was assessed on an NVIDIA Jetson Orin device with ONNX and TensorRT optimizations. Results: SegFormer-B4 achieved the highest accuracy (Dice: 0.9843, IoU: 0.9694), but was not selected for clinical deployment due to computational constraints. SegFormer-B2 provided comparable accuracy (Dice: 0.9787, IoU: 0.9588) with significantly faster inference (94 ms per frame), offering an optimal balance suitable for real-time clinical use. SSFormer showed lower accuracy and slower inference, limiting its practical deployment. Conclusions: DeepPolyp enables systematic evaluation of polyp segmentation models, assisting in selecting models based on both performance and computational efficiency. Despite superior accuracy from SegFormer-B4, SegFormer-B2 was selected for clinical deployment due to its advantageous balance between accuracy and real-time execution efficiency. Original Article Mon, 11 Aug 2025 00:00:00 GMT MarcoMameli, SepidehShiralizadeh, MassimilianoPapi, Iulian GabrielColtea, Mon, 11 Aug 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101158 https://www.explorationpub.com/Journals/edht/Article/101159 Aim: Moving beyond the traditional use/non-use dichotomy, this study examines how variations in older adults’ internet use relate to their multidimensional frailty status. Methods: Data were drawn from the Belgian Ageing Studies (BAS), a large-scale cross-sectional survey conducted in Flanders (Belgium) and included 2,312 individuals aged 60 and older. Internet use was categorized into non-users, basic users, selective users and allround users. Multidimensional frailty was assessed using the Comprehensive Frailty Assessment Instrument (CFAI), covering physical, psychological, social and environmental domains. Multinomial logistic regression and Chi-squared automatic interaction detection (CHAID) were conducted. Results: Regression analysis revealed that older adults with mild or high levels of physical frailty, as well as those with high levels of environmental frailty, were more likely to not use the internet. Furthermore, individuals with high physical frailty and high social frailty were more likely to be basic internet users. Social frailty was also linked to allround internet use, with those in the mild and high frailty categories being less likely to be allround users. However, CHAID analysis highlighted that sociodemographic factors—particularly low education and advanced age—are more strongly associated with low internet usage than frailty itself. Conclusions: Multidimensional frailty is associated with internet use, with mild and high frailty groups being less internet savvy. Original Article Fri, 22 Aug 2025 00:00:00 GMT JorritCampens, Petruste Braak, Myo NyeinAung, Nico DeWitte, , Fri, 22 Aug 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101159 https://www.explorationpub.com/Journals/edht/Article/101160 Virtual reality (VR) and digital health technologies have shown increasing potential in addressing psychological challenges such as homesickness and emotional distress, yet the role of emotional bonds, particularly place attachment, in shaping the design and effectiveness of these interventions remains underexplored. This study conceptualizes the integration of place attachment theory into digital health interventions, especially those utilizing VR, and proposes a theoretical and practical framework for designing emotionally resonant virtual environments. Two interrelated conceptual models are introduced: the Virtual Place Attachment Development Model (VPADM), which outlines psychological, social, environmental, and cultural dimensions that contribute to emotional bonding with virtual spaces, and the Cultural Adaptation System for Virtual Environments (CASVE), which addresses cross-cultural adaptation processes through assessment, implementation, and evaluation. These frameworks illustrate how virtual place attachment can be purposefully designed to enhance user engagement and emotional well-being, while also highlighting practical challenges such as accessibility, digital literacy, and the need for culturally responsive content. By integrating place attachment theory into digital mental health design, the paper offers a pathway to improve therapeutic outcomes in VR environments and provides a foundation for researchers and practitioners to develop emotionally supportive, culturally meaningful, and context-sensitive digital health interventions. Review Fri, 05 Sep 2025 00:00:00 GMT ChengchengYin, JacquelineMcIntosh, BrunoMarques, Fri, 05 Sep 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101160 https://www.explorationpub.com/Journals/edht/Article/101163 Aim: In 2023, the average woman used 13 different personal care products (PCPs) daily, exposing them to 114 different chemical toxins, including carcinogens, synthetic preservatives, and fragrances. Parabens, commonly used as preservatives and fragrance ingredients, are found in household and PCPs, such as cosmetics and hair products. Exposure to parabens has been associated with an increased risk of breast cancer and endocrine disorders. This study aims to evaluate the effectiveness of the “Paraben-Free & Me” educational toolkit (OSF Registration DOI: 10.17605/OSF.IO/WXU34) by exploring whether it influences changes of 3.5 points (10%) in paraben-free behaviour in women aged 18–35 years old when compared to the control group. Methods: This study consists of a randomized controlled trial to evaluate the effectiveness of the educational toolkit among 101 female students aged 18 to 35 years old following a four-week intervention period. Baseline and follow-up questionnaires were used to assess participants’ knowledge and access to information, risk perception, health beliefs, and paraben-free behaviour by providing composite scores for each construct. Results: The change in the knowledge and access to information score was significantly greater in the intervention group compared to the control group (+3.96, 95% CI [1.95, 5.96]). There were slight differences between groups in relation to health beliefs (+0.63, 95% CI [–1.02, 2.27]), risk perception (+0.96, 95% CI [–0.61, 2.54]), and paraben-free behaviour (–0.16, 95% CI [–2.88, 2.57]); however, these were not statistically significant. Conclusions: This study suggests that while the “Paraben-Free & Me” educational toolkit was unsuccessful in promoting greater paraben-free behaviour in the intervention group, it can increase women’s knowledge and access to information related to parabens in their PCPs. Future studies can focus on evaluating paraben-free behaviour and the effectiveness of the educational toolkit by exploring biological measures, such as urinary concentrations of parabens, and air pollutant concentrations. Original Article Sun, 28 Sep 2025 00:00:00 GMT GraziellaDe Michino, CarolineBarakat, Sun, 28 Sep 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101163 https://www.explorationpub.com/Journals/edht/Article/101165 The prevalence of tinnitus in veterans is notably higher than in the general population and can significantly disrupt daily life. Given the impact of tinnitus, along with the lack of effective interventions, exploring new approaches is warranted. Wearable sound technologies offer a non-invasive and easily accessible approach. However, limited research has explored the effectiveness of sound therapy in UK veterans. A prior study supported the feasibility and acceptability of a non-invasive wearable device (i.e., TinniSoothe) in a sample of veterans. However, a waitlist-controlled trial is needed to investigate the effectiveness of the device. This waitlist-controlled trial aims to explore the effectiveness of a wearable device in reducing tinnitus symptoms in a sample of UK veterans. Veterans will be randomly allocated to one of two conditions: (1) the immediate intervention condition, which receives the device post-randomisation, or (2) the waitlist control group, which receives the device one-month post-randomisation. The trial will be conducted in veterans (n = 20) who have experienced tinnitus. Participants will be asked to use the device for one month. The immediate intervention group will be compared to the waitlist control group. The primary outcome is change in tinnitus severity (Tinnitus Functional Index, TFI) and mental health (General Health Questionnaire-12, GHQ-12) from baseline to one-month post-randomisation. Primary and secondary outcomes will be assessed at all timepoints (baseline, one-month post-randomisation, and two-month post-randomisation), while predictor variables will only be assessed at baseline to reduce participant burden. Recruitment will begin in October 2025. The study is expected to take 12 months, with results published in 2027. This study explores whether a wearable device is efficacious in reducing self-reported symptoms of tinnitus in comparison to a waitlist control group. This innovative approach, if successful, could offer a practical option for reducing tinnitus distress. The trial is registered on clinicaltrials.gov, identifier: NCT06905158. Protocol Wed, 15 Oct 2025 00:00:00 GMT PhoebeHowlett, DominicMurphy, Wed, 15 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101165 https://www.explorationpub.com/Journals/edht/Article/101166 Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge. Original Article Thu, 16 Oct 2025 00:00:00 GMT OlenaLitvinova, Andy Wai KanYeung, JavierEcheverría, YousefKhader, Md. MostafizurRahman, ZafarSaid, KarolinaLach, BhupendraSidar, AnastasiosKoulaouzidis, Adeyemi O.Aremu, Conrad V.Simoben, Hemanth KumarBoyina, Firdous M.Usman, Sheikh Mohammed SharifulIslam, Jayanta KumarPatra, GitishreeDas, GaneshVenkatachalam, HiteshChopra, JosefNiebauer, AhmedFatimi, Alexandros G.Georgakilas, Mohammad RezaSaeb, Doris E.Ekayen, Kennedy O.Abuga, MichałŁawiński, YueQiu, Eliana B.Souto, GuanqiaoLi, Hari PrasadDevkota, WeizhiMa, Jamballi G.Manjunatha, Nikolay T.Tzvetkov, Rupesh K.Gautam, MaimaMatin, OlgaAdamska, GeorgeKoulaouzidis, Farhan BinMatin, Bodrun NaherSiddiquea, DongdongWang, JivkoStoyanov, Jarosław OlavHorbańczuk, KamilWysocki, Emil D.Parvanov, Michel-EdwarMickael, ArturJóźwik, NataliaKsepka, Smith B.Babiaka, Bey HingGoh, Tien YinWong, Benjamin S.Glicksberg, Laszlo BarnaIantovics, MarcinŁapiński, ArturStolarczyk, FabienSchultz, Stephen T.Wong, RonanLordan, Faisal A.Nawaz, Rajeev K.Singla, ArunSundarMohanaSundaram, HimelMondal, AyeshaJuhi, ShaikatMondal, MerisaCenanovic, ElisaOpriessnig, ChristosTsagkaris, RonitaDe, Siva SaiChandragiri, RobertasDamaševičius, MarcoCascella, GiuseppeLisco, VincenzoTriggiani, Olga EugeniaDisoteo, Atanas G.Atanasov, Thu, 16 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101166 https://www.explorationpub.com/Journals/edht/Article/101167 The World Health Organization (WHO) estimates that unsafe food is responsible for 600 million cases and over 400,000 deaths annually. Traditional outbreak investigations are often time-consuming, inefficient, and limited by the quality and timeliness of available data. The integration of artificial intelligence (AI), such as machine learning, offers innovative approaches to improve the accuracy, speed, and efficiency of foodborne disease surveillance and outbreak detection. We conducted a mini review of the published literature and explored the potential applications of AI in foodborne disease prevention and control. Key areas explored included predictive analytics, food supply chain monitoring, public health surveillance, and laboratory-based investigations. AI-based predictive models support improved monitoring of environmental risk factors, better management of food supply chains, and more timely detection and prevention of contamination and outbreaks. We also described several challenges related to the integration of AI in food safety systems, including data quality, regulatory frameworks, and ethical considerations. By integrating advanced AI-driven methods, the future of food safety promises greater efficacy and equity in public health. Mini Review Tue, 21 Oct 2025 00:00:00 GMT SabineMaritschnik, BernhardBenka, AndrewAoun, LukasRichter, AliChakeri, VivienBrait, AlinaNovacek, Adriana CabalRosel, Maria Marinho DiasCardoso, ZiadEl-Khatib, Tue, 21 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101167 https://www.explorationpub.com/Journals/edht/Article/101169 Aim: Obsessive-compulsive disorder (OCD) is a mental health condition that significantly interferes with the school environment. The concealment of symptoms, lack of identification, and limited knowledge about the disorder often lead to delays in help-seeking, which are associated with greater chronicity, increased interference, and poorer treatment response. Programmes that educate teachers on early detection of OCD could help identify children at risk and promote help-seeking behavior. This study analyzed the feasibility, acceptability, and preliminary efficacy of the health app esTOCma from both quantitative and qualitative perspectives among teachers, as well as explored areas for improvement. Methods: A total of 19 teachers (mean age = 47.74 years, SD = 11.2) completed the intervention along with pre- and post-intervention assessments through the app. In addition, they responded to open-ended questions to share their opinions about the app. Results: Teachers took an average of 4.89 days (SD = 4.21) to complete the intervention. The app demonstrated excellent usability (M = 85.5, SD = 10.3) and was found useful by the majority of participants (89.5%), who reported satisfaction (84.2%) and stated they had learned considerably (73.7%) through its use. Upon completing the intervention, participants showed greater understanding of OCD and its treatments (MHLQ-R: z = –2.92, p = 0.004), lower levels of stigma (AQ-9: z = –3.67, p < 0.001), and a higher intention to seek professional help in case of experiencing obsessive-compulsive symptoms (GHSQ: z = –2.50, p = 0.012). Conclusions: esTOCma appears to be a feasible app in an educational context, showing high acceptability among participating teachers. Moreover, the app increases knowledge and understanding of OCD, promotes the intention to seek professional help, and reduces stigma toward the disorder. Several improvements are suggested to further enhance the app’s potential impact in educational settings. Original Article Thu, 30 Oct 2025 00:00:00 GMT LucíaBellver-Peñalver, SandraArnáez, GemmaGarcía-Soriano, Thu, 30 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101169 https://www.explorationpub.com/Journals/edht/Article/101168 Aim: This study analyses the time series of daily increases in the number of diagnosed COVID-19 cases in Russia and countries from different continents. The aim of the study is to identify the specifics of the population response of different countries to the spread of the pandemic and anti-epidemic measures of public authorities to determine the most effective model to describe this process. This is a problematic, synoptic, and pilot study. Methods: To evaluate this response strategy, models and methods from reliability theory are used to describe the probability of health protection, the probability density function of an increasing number of cases, the integrated risk of infection, the risk of morbidity, the acceptable risk, and the manageability of the epidemic situation. To approximate infection curves, various daily incidence rate functions are used and compared, and their coefficients are calculated for various pandemic waves. Results: The results demonstrate that the Fréchet distribution function is the best model for the epidemic process. Indicators of variability in acceptable risk were identified during the first stage of pandemic development, showing the varying controllability of the situation by health systems. Through meta-analysis, country distributions were shown to appear as a single pattern, abstracted from local conditions. Estimated coefficients of reliability functions allow the construction of cartograms that reflect the peculiarities of state epidemic regulation and the stages of global pandemic deployment. Conclusions: The findings confirm the effectiveness of the selected model in terms of reliability theory and identify directions for model improvement, taking into account the dynamic nature of the pandemic and its specific characteristics in different countries. The study is based on the methodological approach of function stratification (geometric fiber bundle). It allows for a deeper understanding of the identified patterns within a broader knowledge system. Original Article Wed, 29 Oct 2025 00:00:00 GMT Aleksander KCherkashin, Natalia EKrasnoshtanova, Wed, 29 Oct 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101168 https://www.explorationpub.com/Journals/edht/Article/101170 Aim: Designed to support the mental well-being of university students, the gamified Student Stress Resilience (SSResilience) app guides users in setting and working toward goals related to studying, socializing, and exercising. The app monitors progress through a combination of data from a user’s phone (via Internet of Things sensors) and information they enter themselves. This efficacy study documented students’ goal-setting efforts (RQ1) and examined the app’s effect on students’ anxiety, resilience, and psychological well-being (RQ2). Methods: A quasi-experimental pretest-posttest control group design was used. Experimental group students (n1 = 25) used the app for two weeks. Control group students (n2 = 50) used different means to set the same goals. All students were pre-tested and post-tested on anxiety, well-being, and resilience using standardized questionnaires. Results: Nineteen out of 25 experimental group students used the app to set one or more goals (19/25), and 18 of them found it helpful (18/25). The experimental group experienced a significant (t(22) = 2.72, P = 0.013) decrease in anxiety from Mpre = 8.96 (SD = 5.30) to Mpost = 5.76 (SD = 4.59), an increase in well-being from Mpre = 54.6 (SD = 25.88) to Mpost = 65.12 (SD = 23.90), but no change in resilience. Control group students’ (n = 43) measurements remained unchanged. Conclusions: Preliminary findings indicate a potential value of the SSResilience app for significantly reducing students’ anxiety and increasing their well-being. Integrating Internet of Things technology (built-in phone sensors) into gamified apps for health holds significant promise by offering valuable data to users, app developers, and researchers. Future research will use wearables to measure stress and physical activity more accurately than self-reports. Original Article Thu, 06 Nov 2025 00:00:00 GMT IolieNicolaidou, DespoNicolaidou, Thu, 06 Nov 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101170 https://www.explorationpub.com/Journals/edht/Article/101171 We describe the rationale for and design of a non-inferiority trial to evaluate the relative effectiveness of electronic alcohol screening with in-person vs. electronic brief intervention (BI) approaches implemented in Alexandra Township, South Africa, and Zacatecas-Guadalupe, Mexico. The purpose of screening and brief intervention is to identify individuals whose responses to the Alcohol Use Disorders Identification Test (AUDIT) indicate risky drinking patterns and offer them information and advice to help them reduce their drinking. We seek to determine whether a BI comprising information and advice delivered electronically, along with the opportunity to schedule an appointment with a health care professional at a later time, is not significantly worse than a more labor-intensive traditional BI provided through a face-to-face interaction with a health professional immediately following screening. Selected patients visiting participating health clinics in Alexandra and Zacatecas-Guadalupe will be asked to complete the AUDIT screening using an online app accessed via a handheld device. Those whose scores indicate risky alcohol consumption will be invited to participate in the study. Participants at the clinics will be allocated in alternate weeks to either a customary in-person BI or an electronic BI. Based on power analyses taking attrition and nesting within clinics into account, the target sample sizes are 680 in Alexandra and 560 in Zacatecas-Guadalupe. Measures of 30-day alcohol consumption and AUDIT scores will be obtained at baseline, 3 months, and 6 months. The primary outcome will be the past 30-day quantity-frequency of alcohol consumption. Outcomes will be compared for the two study conditions using mixed effects multilevel regression analyses to account for nesting of observations within participants and participants within clinics. Potential socio-demographic covariates include gender, age, marital status, the highest completed level of education, family’s primary native language (a proxy for ethnicity/culture), presence of household members younger than 16, and subjective economic status (Trial ID: NCT07150156. Clinical trial platform: ClinicalTrials.gov Protocol Registration and Results System. Web address: https://clinicaltrials.gov). Protocol Mon, 10 Nov 2025 00:00:00 GMT Deborah A.Fisher, Christopher L.Ringwalt, Joel W.Grube, Ted R.Miller, Mon, 10 Nov 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101171 https://www.explorationpub.com/Journals/edht/Article/101173 Aim: To assess the burden of digital eye strain (DES) and associated factors among technology students at public universities in southern Ethiopia. Methods: A cross-sectional study was conducted from March to April 2024 at three universities—Hawassa, Dilla, and Jinka. Data were collected using pretested self-administered questionnaires, including the Digital Eye Strain Questionnaire and other relevant variables. Results: The survey tool was distributed to the total study population of 788 students, of whom 403 completed the survey, representing 93.5% of the calculated sample size of 431. Participants were predominantly male (74.7%) and enrolled at Hawassa University (57.8%). Daily digital device usage of ≥ 2 hours was reported by 259 (64.3%), and 72.7% had owned digital devices for > 2 years. Few participants reported smoking (0.7%), alcohol use (10.9%), or khat chewing (7.7%), and 13.9% had a history of accidents. Overall, 68.5% [95% confidence interval (CI): 64.0%–73.0%] experienced at least one symptom of DES in the past 12 months, with photophobia being the most common. DES was experienced more likely among students from Hawassa University [adjusted odds ratio (AOR) = 2.43; 95% CI: 1.11–5.30; p = 0.026], females (AOR = 2.32; 95% CI: 1.25–4.31; p = 0.008), current alcohol consumers (AOR = 3.12; 95% CI: 1.20–8.08; p = 0.019), and those with a history of accidents (AOR = 2.68; 95% CI: 1.17–6.13; p = 0.020). Conclusions: Over two-thirds of final-year technology students in southern Ethiopian universities reported at least one symptom of DES, with higher risk observed among females, alcohol users, and those with prior accidents. Original Article Mon, 10 Nov 2025 00:00:00 GMT Melaku HaileLikka, SamsonAlemayehu, DejeneHurissa, BetelhemEshetu, TesfayeBayu, TamiratYenealem, HannaGetachew, Mahlet AshenafiArgaye, AbateYesigat, SamuelAssefa, TewelgnKebede, Mon, 10 Nov 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101173 https://www.explorationpub.com/Journals/edht/Article/101172 Aim: Generative text-to-image technologies offer new opportunities for individuals to visually articulate internal experiences. While traditional artistic self-portraiture has been extensively associated with self-insight, its AI-assisted equivalent remains underexplored. This study investigates the experiential and assessment potential of AI-generated self-representations and assesses their applicability within contemporary digital mental health frameworks. Methods: Five participants (aged 18–58) engaged in a 45-minute image generation session using Midjourney, producing approximately 500 images. This was followed by semi-structured interviews analyzed via interpretative phenomenological analysis (IPA). This study is idiographic and exploratory, drawing on the principles of IPA. Our aim is an in-depth explication of lived experience and shared experiential layers across cases. The findings should therefore be read as hypothesis-generating and as groundwork for future, larger-scale and mixed-methods evaluations and potential telepsychology integrations. Results: Three group experiential themes: (1) images as functional tools (e.g., as sources of comfort or aspirational vision boards); (2) self-reflective space (facilitating spontaneous self-disclosure and novel insight); and (3) modalities of self-definition (symbolic representation and narrative arc). Participants described the process as highly engaging and reported enhanced self-efficacy. Conclusions: AI-assisted image generation presents a flexible and user-centered modality for psychological reflection, with potential to augment art- and narrative-based therapeutic interventions. Ethical measures (e.g., anonymized data handling, withdrawal options) proved viable. Further research should explore larger, diverse samples and examine integration within telepsychology platforms to assess clinical utility. Original Article Mon, 10 Nov 2025 00:00:00 GMT LilienTóth, KlausKellerwessel, AdriennUjhelyi, Mon, 10 Nov 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101172 https://www.explorationpub.com/Journals/edht/Article/101174 The integration of artificial intelligence (AI) into psychiatric care is rapidly revolutionizing diagnosis, risk stratification, therapy customization, and the delivery of mental health services. This narrative review synthesized recent research on ethical issues, methodological challenges, and practical applications of AI in psychiatry. A comprehensive literature search was conducted with no limitation to publication year using PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed articles and grey literature related to the integration of AI in psychiatry. AI enhances early identification, predicts relapses and treatment resistance, and facilitates precision pharmacopsychiatry by leveraging data from machine learning, natural language processing, digital phenotyping, and multimodal data integration. This review highlights the advancements in the integration of AI in psychiatric care, such as chatbot-mediated psychotherapy, reinforcement learning for clinical decision-making, and AI-driven triage systems in resource-constrained environments. However, there are still serious concerns about data privacy, algorithmic bias, informed consent, and the interpretability of AI systems. Other barriers to fair and safe implementation include discrepancies in training datasets, underrepresentation of marginalized groups, and a lack of clinician preparedness. There is a need for transparent, explainable, and ethically regulated AI systems that enhance, rather than replace, human decision-making. A hybrid human-AI approach to psychiatry is recommended to address these limitations, while interdisciplinary studies, strong validation frameworks, and inclusive policymaking are needed to guarantee that AI-enhanced mental health treatment continues to be effective, fair, and reliable. Review Wed, 19 Nov 2025 00:00:00 GMT Olalekan JohnOkesanya, Uthman OkikiolaAdebayo, IfeanyiNgwoke, Abdulmajeed OpeyemiAgboola, Faith AyobamiAtewologun, Serah BosedeAjayi, Noah OlabodeOlaleke, Tolutope AdebimpeOso, Don EliseoLucero-Prisno, Wed, 19 Nov 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101174 https://www.explorationpub.com/Journals/edht/Article/101175 Aim: Patients diagnosed with irregular astigmatism often require specific methods of vision correction. Among these, the use of a rigid gas permeable (RGP) lens is considered one of the most effective treatment approaches. This study aims to propose a new automated method for accurate RGP lens base-curve detection. Methods: A multi-modal feature fusion approach was developed based on Pentacam images, incorporating image processing and machine learning techniques. Four types of features were extracted from the images and integrated through a serial feature fusion mechanism. The fused features were then evaluated using a multi-layered perceptron (MLP) network. Specifically, the features included: (1) middle-layer outputs of a convolutional autoencoder (CAE) applied to RGB map combinations; (2) ratios of colored areas in the front cornea map; (3) a feature vector from cornea front parameters; and (4) the radius of the reference sphere/ellipse in the front elevation map. Results: Evaluations were performed on a manually labeled dataset. The proposed method achieved a mean squared error (MSE) of 0.005 and a coefficient of determination of 0.79, demonstrating improved accuracy compared to existing techniques. Conclusions: The proposed multi-modal feature fusion technique provides a reliable and accurate solution for RGP lens base-curve detection. This approach reduces manual intervention in lens fitting and represents a significant step toward automated base-curve determination. Original Article Mon, 08 Dec 2025 00:00:00 GMT LeylaEbrahimi, HadiVeisi, EbrahimJafarzadehpour, SaraHashemi, Mon, 08 Dec 2025 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101175 https://www.explorationpub.com/Journals/edht/Article/101178 Artificial intelligence (AI) is transforming healthcare by equipping clinicians and patients with tools that support more efficient, patient-centered care. In pediatrics, however, the implementation of AI demands a higher threshold for responsibility, transparency, and family-centered engagement. This perspective explores the opportunities and challenges of AI in pediatric healthcare, highlighting the unique ethical and developmental considerations that distinguish children’s care from adult medicine. Drawing on Kaiser Permanente’s seven principles for responsible AI, the article emphasizes the importance of augmentation over automation, the need for pediatric-specific validation, and the necessity of trustworthiness and fairness in clinical deployment. It outlines how AI can support primary care providers through enhanced decision support, early screening for developmental and behavioral disorders, including the potential for AI to create personalized developmental trajectories, moving beyond static population norms to provide earlier, more precise insights into a child’s neurodevelopmental progress, improved electronic health record usability, and risk prediction models. However, without careful governance, AI poses risks of bias, inequity, and erosion of clinician judgment. Policy recommendations include redesigning family consent models, ensuring robust clinician training, and mandating pediatric-specific testing of AI systems with diverse, representative datasets. Ultimately, AI should function as a supportive tool that strengthens, not replaces, human empathy, clinical expertise, and family-centered values. Responsible innovation is essential to ensure that children benefit equitably from AI while maintaining trust, safety, and compassion in pediatric healthcare. Perspective Tue, 06 Jan 2026 00:00:00 GMT Venkata SushmaChamarthi, RamandeepKaur, RahulKashyap, Tue, 06 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101178 https://www.explorationpub.com/Journals/edht/Article/101177 Vaccines have eliminated once-deadly diseases, yet rising vaccine hesitancy threatens these gains. Human papillomavirus (HPV) illustrates this crisis: Although it is one of the few vaccines that directly prevents cancer, uptake remains low in the United States and globally, particularly in regions with high cervical cancer incidence. This persistent gap undermines both individual and public health. This paper examines how digital health technologies, aligned with policy frameworks and community engagement, can address HPV vaccine hesitancy. We propose the Digital Vaccine Advocacy Toolkit, a structured, HPV-focused framework that integrates electronic health record (EHR)-based clinical decision support, personalized reminders, population dashboards, AI-driven misinformation surveillance, and culturally tailored education. As a conceptual model, it draws on secondary evidence and policy recommendations rather than original empirical data, emphasizing interoperability, privacy safeguards, equity-driven design, and stakeholder engagement to support feasibility across diverse health systems. The Toolkit is organized into illustrative workflows that demonstrate how technical features could be combined with policy mechanisms and financing models to strengthen HPV vaccination. By situating HPV within the World Health Organization’s 90-70-90 elimination targets and the recent adoption of single-dose schedules, the framework highlights both translational relevance and global applicability, though its recommendations require pilot testing and empirical validation. Overall, the Digital Vaccine Advocacy Toolkit offers a practical roadmap for improving HPV vaccine uptake through the integration of technology, policy, and ethics, and provides a transferable model for advancing digital health strategies to increase vaccine confidence and equity in immunization programs worldwide. Perspective Sun, 04 Jan 2026 00:00:00 GMT Precious IretiayoAdeniyi, Sun, 04 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101177 https://www.explorationpub.com/Journals/edht/Article/101176 Aim: The aim of this study is to compare the accuracy, reliability, and educational quality of YouTube videos on osteochondritis dissecans based on their YouTube Health verification status. Methods: The term “osteochondritis dissecans” was searched on June 3, 2024. The first 50 videos found on YouTube after searching “osteochondritis dissecans” were evaluated. The Journal of the American Medical Association (JAMA) benchmark criteria was used to score video reliability and accuracy (0–4 points), the Global Quality Score (GQS) was used to score nonspecific educational content (0–5 points), and the osteochondritis dissecans specific score (OCDSS) was used to score specific educational content (0–11 points). Three independent reviewers scored all videos, and interrater reliability was assessed with intraclass correlation coefficients (ICC). Group differences were analyzed with one-way analysis of variance (ANOVA) and independent sample t-tests, and multivariable linear regression was used to identify independent predictors of JAMA, GQS, and OCDSS scores. Results: A total of 50 videos were analyzed with a cumulative 326,851 views. The mean JAMA score was 2.28 ± 0.64, the mean GQS score was 2.60 ± 1.36, and the mean OCDSS was 5.02 ± 3.16. The mean JAMA score for YouTube Health verified videos was 2.44 ± 0.34, GQS was 2.72 ± 1.22, and OCDSS was 5.72 ± 2.69. The mean JAMA score for videos not verified by YouTube Health was 2.29 ± 0.65, GQS score was 2.61 ± 1.44, and OCDSS was 4.95 ± 3.37. These differences were not statistically significant: JAMA p = 0.380, GQS p = 0.837, OCDSS p = 0.546. Conclusions: There were no significant differences in reliability, educational content, and comprehensiveness between videos that were verified by YouTube Health and videos that were not verified. Original Article Sun, 04 Jan 2026 00:00:00 GMT Taylor M.Low, JacobLin, DanielNewman, DennisChakkalakal, Sun, 04 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101176 https://www.explorationpub.com/Journals/edht/Article/101179 Aim: A comprehensive understanding of current digital literacy and perspectives of the psychiatric workforce is important to introduce appropriate digital psychiatry interventions and implement contextually relevant measures in Pakistan. This study aims to address a gap in the existing literature by assessing psychiatrists’ knowledge, attitudes, perceived barriers, and willingness to integrate digital psychiatry into their clinical practice. Methods: A cross-sectional online survey was conducted from January 2023 to June 2023 across psychiatric departments of 18 public hospitals in Pakistan. The study included psychiatry residents, fellows, and consultants. A 48-item questionnaire, internally and externally validated, assessed knowledge, perceptions, and willingness to adopt digital psychiatry tools—telepsychiatry, artificial intelligence, mental health applications, and virtual reality. Data were analyzed using Statistical Package for the Social Sciences (version 26) for descriptive statistics, correlation, and regression analyses, while thematic analysis of open-ended responses was performed using Quirkos. Results: A total of 200 participants (56.0% aged 20–30 years, n = 112; 55.5% male, n = 111) were part of this study. 68.5% (n = 137) understood the applications of telepsychiatry, while 72.5% (n = 145) agreed that it is time-efficient and cost-effective. Only 39.5% (n = 79) of participants had received relevant artificial intelligence training to incorporate it in their psychiatric clinical practice. 62.0% (n = 124) of respondents reported unfamiliarity with the use of mental health applications. Regarding virtual reality, 32.5% (n = 65) were familiar with the technology, but only 42.5% (n = 85) were aware of its applications in psychiatric care. Thematic reflexive analysis revealed major challenges, including a ‘lack of infrastructure/resources’ (44.5%, n = 89) and a ‘lack of education/awareness’ (21.5%, n = 43). Conclusions: This study represents the first cross-sectional examination of digital psychiatric literacy in Pakistan’s healthcare system, which revealed significant gaps in digital health competencies among psychiatrists. Given the vast potential of emerging technologies in addressing mental health challenges, there is an urgent need for mental health professionals in Pakistan to integrate digitization in psychiatric practice. Original Article Wed, 07 Jan 2026 00:00:00 GMT Mehr Muhammad AdeelRiaz, TahiraAbrar, MuhammadHammad, HanaaTariq, RidaFatima, IrumSiddique, Faisal A.Nawaz, Wed, 07 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101179 https://www.explorationpub.com/Journals/edht/Article/101182 Background: To synthesize evidence on how medical thermography, integrated with artificial intelligence (AI), blockchain, 5G (5th Generation mobile networks), and Internet of Things (IoT), enhances diagnostics, fraud prevention, and personalized health insurance in emerging markets, addressing cost escalation and access gaps. Methods: This systematic review followed AMSTAR 2 and PRISMA guidelines, synthesizing 25 sources (22 peer-reviewed articles, 3 industry reports) from a pre-analyzed dataset. Inclusion focused on relevance to thermography, insurance, or synergistic technologies; exclusions included non-peer-reviewed or irrelevant items. Data extraction via Microsoft Excel (version 2409) covered diagnostics, applications, synergies, and contexts. Quality appraisal used the Mixed Methods Appraisal Tool (MMAT) to assess methodological rigor. Narrative synthesis addressed heterogeneity, without meta-analysis due to design diversity and resource limits. Results: Thermography achieves 83–98% sensitivities for breast cancer (asymmetries > 3.0°C), diabetic foot ulcers (DFUs; 96.71% with AI), and rheumatoid arthritis (RA; inflammation > 0.5°C), reducing triage times by 25% and costs by 30% in mobile settings. Blockchain’s six-layer architecture, with Practical Byzantine Fault Tolerance and InterPlanetary File System, secures data at US$0.028 per transaction, potentially reducing fraud through enhanced verification. In emerging markets like India and Brazil, portable thermography with 5G supports screening, aligned with standards like T/ZADT 005-2002. Discussion: These integrations enable early detection (saving US$8,000–12,000 per DFU), fraud mitigation, and equitable access, though protocol variances and biases require attention. Recommendations include standardization, pilots in rural areas, and bias-mitigating AI frameworks to optimize health insurance outcomes. Systematic Review Mon, 19 Jan 2026 00:00:00 GMT Enzo MontresolFaversani, Mon, 19 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101182 https://www.explorationpub.com/Journals/edht/Article/101180 Aim: Diagnosing and treating major depressive disorder (MDD) remains a pressing global health challenge. Generative-AI tools, by lowering technical barriers and offering rapid visual feedback, may open new avenues for art-based assessment and intervention. Methods: In this exploratory qualitative pilot, we conducted reflexive thematic analysis of semi-structured interviews with N = 10 young adults at elevated risk for depression who generated self-representative images in Midjourney during a 45-minute session. Participants were selected from a larger cohort described elsewhere; no quantitative analyses were conducted in the present paper. Results: Qualitative findings suggested therapeutic-like mechanisms that mirror—and in some cases amplify—those reported for traditional art therapy, including the experience of flow and spontaneity, a heightened sense of creative agency, and the safe externalization of difficult or extreme emotions. Some participants described abrupt “sentiment switches,” where joyful imagery was immediately followed by scenes of sudden, intrusive self-criticism. Importantly, the generative process also surfaced idiosyncratic “resource images” (e.g., nature motifs, hobbies, values, loved ones) that participants experienced as calming or empowering, hinting at personalised anchors for future interventions. Conclusions: In line with prior quantitative work showing that more negative prompt sentiment statistically relates to higher BDI scores, the present qualitative narratives offer an interpretive account of how such negativity may emerge during AI-assisted self-representation. However, the current study does not integrate datasets or perform mixed-methods triangulation and uses those prior findings solely for contextualization. We conclude that, with appropriate ethical safeguards, generative-AI image making may serve as a flexible, low-cost adjunct to existing diagnostic and art-therapeutic practices, offering clients and clinicians a shared visual language for exploring the multi-layered experience of depression. Original Article Mon, 12 Jan 2026 00:00:00 GMT KlausKellerwessel, AsztrikKovács, AdriennUjhelyi, Mon, 12 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101180 https://www.explorationpub.com/Journals/edht/Article/101181 Digital twin technology is emerging as a transformative paradigm in healthcare, shifting practice from provider-centered models toward more personalized forms of medicine. As dynamic virtual representations of the human body, digital twins integrate biometric data, lifestyle patterns, and clinical records to simulate, monitor, and predict health trajectories in real time. Their growing use raises not only technical possibilities but also important questions about how patients relate to these data-driven counterparts, particularly when twins inform everyday health decisions in chronic care, such as diabetes or oncology. This perspective examines these relational dynamics and their ethical, cultural, and experiential implications for autonomy, decision-making, and the lived experience of being represented in data. To guide this analysis, we introduce a scale framework with three intersecting lenses: time, distinguishing asynchronous from synchronous updating; twining, ranging from close mirroring to more augmentative forms of representation; and control, spanning human-led to twin-driven decision authority. Using this framework, we position four common types of digital twins: mirror, shadow, intelligent, and simulacra as an evolution from basic representation to transformative modeling. We argue that future healthcare and public health policy must go beyond technical innovation to address patients’ lived experiences, ensuring that digital twins enhance rather than diminish autonomy, trust, and equity. This perspective thus calls for a patient-centered approach in designing and implementing digital twin technologies. Perspective Wed, 14 Jan 2026 00:00:00 GMT KerenMazuz, SeemaBiswas, Wed, 14 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101181 https://www.explorationpub.com/Journals/edht/Article/101183 Aim: This study aims to evaluate the outreach achieved by psychiatry-related posts using the hashtag #YouthMentalHealth, highlighting how social media platforms can shape public discourse on adolescent mental health. Methods: We utilized the Fedica research analytics tool to characterize posts containing #YouthMentalHealth from January 10, 2018, to January 10, 2023. This analysis examined the #YouthMentalHealth activity timeline, identifying the number of posts containing the hashtag and the geographical distribution to assess the effectiveness of hashtag campaigns. Results: The #YouthMentalHealth movement resulted in 58,000 posts shared by around 25,000 X users, generating 292.7 million impressions (views). The top three countries from which most posts containing #YouthMentalHealth were shared included the United States (35.14%), Canada (29.15%), and the United Kingdom (14.37%). The three largest contributor groups were management companies (20.6%), educational advocacy organizations (17.5%), and social advocacy groups (14%). Conclusions: This first-of-its-kind study explores the impact and utilization of #YouthMentalHealth globally, reporting trends and patterns from digital media platforms. By mapping the hashtag’s global footprint, the study offers novel insights into how digital advocacy can amplify youth mental health awareness and connect multidisciplinary stakeholders. These findings contribute to emerging frameworks in digital psychiatry by underscoring the role of social media as a complementary tool for mental health promotion and community engagement, while illuminating diverse strategies to aid the psychiatric community in effectively addressing the mental health needs of adolescents. Original Article Tue, 20 Jan 2026 00:00:00 GMT Mehr Muhammad AdeelRiaz, Ala’aAzayem, Abdul RahmanKhan, NourEl-Basyouny, OlenaLitvinova, Atanas G.Atanasov, Faisal A.Nawaz, Tue, 20 Jan 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101183 https://www.explorationpub.com/Journals/edht/Article/101184 Aim: Neonatal jaundice or neonatal hyperbilirubinemia is a common medical condition impacting newborns and pathological jaundice if left untreated, leads to neurological encephalopathy and/or death. The majority of pathological jaundice cases occur in low and middle- income countries (LMIC). Phototherapy has been determined to be the safest and most effective treatment for jaundice. Although inexpensive light-emitting diodes are available on the market, commercial phototherapy devices are expensive (~US$2,000), which creates a barrier to access for these devices in LMIC. Efforts to construct cost-effective phototherapy units have been implemented in the past, but need a method to validate the intensity and wavelength of light received by the infant at a distance away from the source. Methods: To enable low-cost phototherapy units to be used clinically, this study provides an open-source, low-cost, distributed manufacturing approach to create a light sensor to calibrate phototherapy units. This instrument is a necessary component of any open-source phototherapy treatment used in a clinical setting. This novel instrument was validated by comparing its irradiance and wavelength reading to the commercially calibrated Ocean Insight UV-VIS spectrometer under varying lighting conditions, including that of the existing Datex-Ohmeda Giraffe Spot PT Lite phototherapy equipment accessible through Victoria Children’s Hospital Neonatal Care Ward in London, Ontario, and Kiambu County Hospital in Kenya. Results: The results of this study have demonstrated that for under US$150, a phototherapy calibration device can be constructed capable of measuring up to 200 uW/cm2/nm with an accuracy of 98.6% and detect the peak wavelength within ±12.5 nm. Conclusions: It can be concluded that 3D printed open-source irradiance meters are a viable option for calibrating phototherapy units in LMIC to treat hyperbilirubinemia. Original Article Mon, 09 Feb 2026 00:00:00 GMT Joshua T.M.Givans, AugustineWaswa, JuneMadete, Joshua M.Pearce, Mon, 09 Feb 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101184 https://www.explorationpub.com/Journals/edht/Article/101185 Multicenter imaging studies are increasingly critical in epidemiology, yet variability across scanners, acquisition protocols, and reconstruction algorithms introduces systematic biases that threaten reproducibility and comparability of quantitative biomarkers. This paper reviews the major sources of heterogeneity in MRI, CT, and PET-CT data, highlighting their impact on epidemiologic inference, including misclassification, reduced statistical power, and compromised generalizability. We outline harmonization strategies spanning pre-acquisition standardization, phantom-based calibration, post-acquisition intensity normalization, and advanced statistical and machine learning methods such as ComBat and domain adaptation. Illustrative examples from MRI flow quantification and radiomic feature extraction demonstrate how harmonization can mitigate site effects and enable robust large-scale analyses. Perspective Tue, 10 Feb 2026 00:00:00 GMT NurmakhanZholshybek, LazzatBastarbekova, Tue, 10 Feb 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101185 https://www.explorationpub.com/Journals/edht/Article/101186 Background: Sepsis is a major cause of disease worldwide. Mobile applications (apps) have been developed to assist clinical practice. Current evidence evaluating such apps is diverse. This scoping review aimed to map currently available literature investigating the usage of mobile apps for sepsis-related healthcare. This will highlight evidence gaps, and areas for future innovation and app development. Methods: Databases MEDLINE, Embase, CINAHL, Cochrane, Scopus, and Web of Science were searched in June 2023 (updated in July 2024). Studies containing original research investigating mobile apps for sepsis-related healthcare were included and analysed in three categories identified from the primary purpose of the app: (1) education and awareness, (2) clinical assistance, and (3) biomarker or pathogen detection. Results: A total of 1,755 studies were identified and 27 included following screening, of which 19 (70%) were published in 2020 or later. Most of the 27 studies investigated apps for clinical assistance (70%, n = 19). These apps were diverse, acting as digital solutions for data collection (n = 2), triage (n = 6), clinical guideline access (n = 5), alert delivery (n = 1), and outcome prediction (n = 5). There were five apps (19%) used to assist biomarker or pathogen detection. Of these, most (80%, n = 4) mobile apps were used to detect and quantify colorimetric signals in combination with assays, and all five apps had attachments necessary for laboratory processes. Lastly, three apps (11%) were designed to enhance education and awareness, two targeting medical education and one targeting public awareness. Discussion: Mobile applications offer innovative and exciting digital solutions for biomarker detection, education, and clinical support in sepsis-related healthcare. Current literature is highly heterogenous and rapidly developing. Systematic Review Fri, 13 Feb 2026 00:00:00 GMT KhaliaAckermann, DhruvKhanna, VincentLam, LingLi, Fri, 13 Feb 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101186 https://www.explorationpub.com/Journals/edht/Article/101187 Aim: To assess healthcare professionals’ awareness, attitudes, and utilization of community-based digital health platforms for preventive care in underserved districts of Khyber Pakhtunkhwa, Pakistan, and to identify key barriers associated with routine use. Methods: A cross-sectional survey was conducted between December 2024 and February 2025 among 400 healthcare professionals (doctors, nurses, and allied health practitioners) working in primary, secondary, and tertiary facilities in Swabi and Mardan. Participants were recruited using purposive, stratified (quota-based) sampling. The questionnaire captured knowledge/awareness, attitudes, self-reported utilization, and perceived barriers (infrastructure, training, and privacy). Descriptive statistics were produced, and multivariable regression was used to examine factors associated with utilization. Results: Among the 400 respondents, 332 (83.0%) reported awareness of digital health platforms and 312 (78.0%) reported positive attitudes toward their use. Overall, 297 (74.3%) reported using digital health platforms in practice. The most frequently reported barriers were lack of infrastructure (n = 309, 77.3%), limited training (n = 297, 74.3%), and data privacy concerns (n = 295, 73.8%). In the adjusted logistic regression model, greater knowledge of digital health platforms was associated with higher odds of routine use (aOR = 10.56, 95% CI: 2.36–47.35; p = 0.002), whereas attitude and infrastructure barriers were not significant (p > 0.05). Conclusions: Healthcare professionals in Swabi and Mardan reported high awareness and favorable attitudes toward community-based digital health platforms, but infrastructure gaps, limited training, and data privacy concerns were common barriers. Greater platform knowledge predicted routine use. Strengthening facility readiness, workflow-based training, and practical safeguards to address data privacy concerns may enable safer, more equitable scale-up; findings are context-specific due to non-probability sampling. Original Article Thu, 26 Feb 2026 00:00:00 GMT FaseehIqbal, SamiIqbal, UmarFarooq, SadiaNawaz, MuhammadHammad, KhadijaShakoor, FatimaNoreen, Thu, 26 Feb 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101187 https://www.explorationpub.com/Journals/edht/Article/101188 Background: Urine screening is a critical diagnostic tool in healthcare that supports the detection of a wide range of health conditions, including kidney diseases, metabolic disorders, and infections. Traditionally, urine tests are performed in clinical settings with results that often take time to be delivered. Such delays can hinder timely diagnosis, treatment initiation, and effective disease management. Recent advancements in digital health technologies, particularly the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) algorithms, create opportunities for real-time data acquisition, integration, and analysis within routine urine screening. This systematic review synthesizes the current landscape of IoT-enabled urine screening technologies and evaluates their clinical, engineering, and computational foundations. The review also examines their integration with digital health architectures, edge computing systems, and tech driven personalized care. Methods: A structured literature search was conducted across PubMed, IEEE Xplore, Scopus, and Google Scholar for studies published between 2000 and 2025. Predefined search terms related to urinalysis, IoT, digital health, and microfluidics were applied. Sixty-five studies met the inclusion criteria. Data extraction focused on sensor technologies, digital health platforms, and reported case studies that demonstrated successful system deployment across diverse healthcare settings. Results: IoT-based urine screening technologies support real-time monitoring of biomarkers such as glucose, protein, and pH, which are essential for diagnosing conditions including diabetes, kidney disease, and urinary tract infections (UTIs). Emerging devices utilize optical, and acoustofluidic modalities, while BLE, Wi-Fi, and LPWAN serve as the primary connectivity standards. Discussion: IoT-driven digital transformation demonstrates strong potential to enhance the accessibility, efficiency, and diagnostic accuracy of urine screening. The convergence of biosensing, microfluidics and HDTs enables scalable, continuous, and personalized urine monitoring solutions. Despite these advancements, challenges related to data privacy, infrastructure readiness, and regulatory compliance remain significant barriers. Systematic Review Thu, 05 Mar 2026 00:00:00 GMT SujaySingh, Nirmal KumarMohakud, DayanidhiMeher, BaluPalicheralu, HarshavardhanRajagopal, Himadri TanayaBehera, Thu, 05 Mar 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101188 https://www.explorationpub.com/Journals/edht/Article/101189 Aim: The aim of this study is to analyse the digital patient journey in medical tourism, with a particular focus on Ukraine’s experience under conditions of military challenges and global crises. The study examines how digital tools support inclusivity, accessibility, continuity of care, and patient trust, with special attention to rehabilitation services and vulnerable patient groups affected by war. Methods: The study employs a mixed-methods approach combining a review of scientific literature with empirical research and modelling of the digital patient journey. Primary data were collected through an online survey of 150 healthcare consumers and semi-structured interviews with 15 experts representing medical institutions involved in medical tourism. Quantitative and qualitative analyses were used to examine patient experience, inclusivity barriers, and the role of digital services. Results: The results indicate that key stages of the digital patient journey include online information search and clinic selection, remote consultations, digital support for travel and treatment organization, and post-treatment follow-up and rehabilitation. Ukrainian clinics actively implement CRM systems, telemedicine solutions, and digital communication tools, enabling continuous patient engagement even during crisis conditions. At the same time, significant barriers were identified, including limited inclusiveness of digital services, data security concerns, uneven digital literacy, and infrastructural constraints. Based on the findings, a conceptual model of the digital patient journey integrating service quality, inclusivity, and AI-supported personalization was developed. Conclusions: The findings demonstrate that the digital patient journey is becoming critically important for the development of medical tourism under conditions of global uncertainty. The integration of digital tools with inclusive and patient-centred approaches enhances the resilience of medical services, strengthens patient trust, and provides competitive advantages for medical institutions. The proposed model may be useful for countries experiencing military conflicts or systemic crises and contributes to the broader development of digital and inclusive healthcare. Original Article Wed, 11 Mar 2026 00:00:00 GMT LiudmylaBovsh, AllaRasulova, IrynaMykolaichuk, Wed, 11 Mar 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101189 https://www.explorationpub.com/Journals/edht/Article/101190 Telepsychiatry has transitioned from a supplementary modality to a sustained component of contemporary mental healthcare, driven by technological advancement, workforce shortages, and the COVID-19 pandemic. This narrative review synthesizes current evidence on clinical effectiveness, service models, technological integration, and ethical–legal considerations, and contextualizes these domains through institutional implementation experience in Türkiye. Across major diagnostic groups, including mood, anxiety, psychotic, neurodevelopmental, and substance use disorders, published studies generally indicate comparable outcomes and patient satisfaction to face-to-face care when delivered within structured clinical frameworks. We further articulate the theoretical foundations of clinical equivalence, emphasizing language-mediated therapeutic mechanisms, alliance formation in video-based settings, and behavioral factors influencing adherence. The manuscript introduces a system-level perspective for Türkiye, positioning telepsychiatry as a capacity-extending model within geographically uneven workforce distribution. Institutional applications, including disaster response, postpartum screening pathways, and hybrid specialty clinics, illustrate context-sensitive implementation strategies. Emerging innovations such as digital phenotyping, artificial intelligence, and virtual reality are discussed alongside regulatory, equity, and data governance considerations. We conclude that telepsychiatry represents not merely an emergency substitute but an increasingly integrated and policy-relevant model of care. Review Thu, 09 Apr 2026 00:00:00 GMT AilaGareayaghi, EzgiŞişman, Fatma SeherKocaayan, İlayDalkıran, ElifTatlıdil, AslıhanPolat, Thu, 09 Apr 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101190 https://www.explorationpub.com/Journals/edht/Article/101191 Aim: To explore preliminary signals of change associated with a digitalized educational innovation—The Vital House (La Casa Vital)—on psychological flexibility and introspection among prospective secondary-school teachers in Spain, with the broader goal of promoting mental health competencies relevant to adolescent well-being. Methods: A total of 82 students enrolled in a Master’s program in teacher training at a Spanish public university participated in a 10-session intervention over 2.5 months (approximately 20 hours total). The Vital House model, a metaphorical representation of personal identity through “rooms” symbolizing life roles, was adapted into a digital format. Each room included interactive resources designed to address key psychosocial variables, including self‑efficacy, emotional regulation, and cognitive defusion. Participants reflected on their learning histories and the influence of significant figures, including teachers, on adult identity. Pre- and post-intervention measures assessed components of the ACT Hexaflex model (ad-hoc questionnaire) and introspective capacity (Self-Reflection and Insight Scale-Short Form). Results: Paired-sample analyses indicated pre–post differences on five of six ACT processes: values (p = 0.048), mindfulness (p = 0.014), self-as-context (p < 0.001), cognitive defusion (p = 0.034), acceptance (p = 0.019), and on introspective capacity (p = 0.008). Effect sizes were in the small‑to‑moderate range, with Cohen’s d values ranging from 0.22 (small) to 0.42 (moderate). These findings should be interpreted cautiously given the design. Conclusions: In this pilot‑level, single‑group study, Vital House showed preliminary indications of promise for enhancing psychological flexibility and introspection in teacher training. However, the absence of a control/comparison group, the potential influence of concurrent course content, maturation, historical events, and repeated‑testing effects, as well as the lack of post‑intervention follow‑up, limit causal inference and claims about durability. Future controlled studies with follow‑up are warranted to evaluate efficacy, mechanisms, and maintenance, and to assess scalability across educational contexts. Original Article Thu, 16 Apr 2026 00:00:00 GMT CeciliaPeñacoba, PatriciaCatalá, Thu, 16 Apr 2026 00:00:00 GMT https://www.explorationpub.com/Journals/edht/Article/101191