https://www.explorationpub.com/Journals/edht Most Recent Articles : Exploration of Digital Health Technologies. en-us Thu, 01 May 2025 01:32:37 GMT 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/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/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