Journal of Medical Internet Research

Informal caregivers of people with dementia frequently experience substantial psychological burden, including elevated stress and depressive symptoms. eHealth interventions have emerged as a scalable solution to support caregivers. However, their effectiveness and the influence of intervention characteristics remain unclear.

Emergency department (ED) visits have risen in the United States, with demand for emergency care exceeding supply. Resultant ED crowding harms patients, causes staff burnout, and places financial strain on hospitals, payers, and patients alike. Digital tools, including those leveraging artificial intelligence (AI), offer promise for driving efficiency and mitigating the harms of crowding. However, economic frameworks for evaluating these tools remain underdeveloped, limiting adoption.

Vision-enabled large language models (VE-LLMs) have the potential to provide flexible and explainable medical image interpretation. However, their real-world performance on clinical data, such as 12-lead electrocardiograms (ECGs), has not been systematically assessed.

While digital health technologies promise to reshape the medical journey, their potential might not be realized due to unforeseen implementation challenges. Notably, the future impact of artificial intelligence (AI) in virtual consultations has been poorly investigated.

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The fast growth of social media mining in health research has contributed to an invaluable but quite fragmented body of literature. As the amount of unstructured patient-reported data grows, traditional bibliometric analyses face methodological limitations, particularly regarding synonym fragmentation and arbitrary parameter selection. In their recent publication, “Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis,” Yang and Bohnet-Joschko attempt to address these flaws by introducing a semantic-structural (hybrid) bibliometric framework. This commentary evaluates the methodological innovations of their study and its departure from traditional syntactic keyword-matching tools. By combining citation-informed transformers (SPECTER2) and biomedical language models (PubMedBERT) and dimensionality reduction and density-based clustering, the authors created a reproducible pipeline. In their architecture, they start with foundational machine learning (statistical validity) before transitioning into large language models for qualitative synthesis. I will attempt to explain how this transition from syntactic mapping to semantic vector representation solves known challenges in evidence synthesis, naturally grouping conceptual synonyms without artificially forcing boundaries on the literature. Furthermore, I examine the practical implications of their temporal findings. Such real-time social media mining applications can be very useful for retrospective reporting and evaluating targeted public health interventions. While this pipeline offers high generalizability across disciplines, it also introduces a computational literacy barrier to some, and this re-emphasizes the need for data literacy for health professions. Ultimately, the study provides a transparent approach to informatics because mathematically validated frameworks are foundational for the future of evidence-driven public health policy and clinical decision-making.

Generative artificial intelligence (GenAI) has increasingly entered psychiatric practice through patient-facing chatbots, self-help tools, and clinician-facing workflow support. Although prior research has examined clinicians’ attitudes, readiness, and anticipated use cases, less is known about how frontline encounters with GenAI shape psychiatrists’ interpretations and implementation priorities. Health care foresight also remains methodologically underdeveloped and has focused mainly on external signals, overlooking clinically consequential signals emerging from everyday practice. This gap is especially important in psychiatry, where GenAI-related benefits and harms may depend on patient vulnerability, crisis sensitivity, and the therapeutic relationship.

Large language models (LLMs) require specialized methodologies to quantify model confidence for safe deployment in health care systems; however, there is a lack of established methods for confidence assessment.

Diabetes self-management education and support (DSMES) programs can improve health outcomes, but engagement is often low. “Healthy Living” is a web-based self-management program for people with type 2 diabetes, based on the “HeLP-Diabetes” intervention, which demonstrated effectiveness in a randomized controlled trial. Healthy Living was commissioned by the National Health Service in England and rolled out nationally into routine care in 2020. The program comprises web-based structured learning, unstructured articles (which users could access at any time), and tracking tools such as goal setting. It is important to assess not only the uptake of digital interventions but also the amount of time spent using the intervention and the content they engage with. There is currently limited research on the extent to which people engage with digital DSMES program content outside of a trial setting.

Diabetes is a significant global public health concern, with disproportionately high prevalence and poor access to care in low-resource settings, notably in Sub-Saharan Africa.

Acquired brain injuries are injuries that occur after birth and are a leading cause of long-term disability and death in children and young adults. They may result from trauma, hypoxia, stroke, infection, or a variety of other causes. Fatigue is one of the most common and underrecognized consequences of pediatric acquired brain injury, often expressed behaviorally rather than verbally. Traditional rehabilitation programs are frequently static and cognitively demanding, limiting engagement and therapeutic outcomes. Extended reality (XR) technologies offer new opportunities to address these challenges by enabling interactive, adaptive, and motivating home therapy environments. However, few XR systems are co-developed with children and therapists, and there is limited knowledge about how to co-design engaging, gamified, XR-based motor rehabilitation solutions that take into account children’s fatigue.