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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.