Journal of Medical Internet Research

Large language models (LLMs) are increasingly used by patients for health information and preliminary medical advice. In patient-facing consultations, users may present explicitly stated diagnostic preferences or symptom narratives emphasizing a preferred explanation. Such cognitively biased input constrains the diagnostic context available to the model and may systematically steer its reasoning during interactive LLM-supported health consultations.

Little is known about (1) sociodemographic, psychosocial, or smoking-related differences among individuals recruited to smoking cessation randomized controlled trials (RCTs) using in-person versus online recruitment methods or (2) the relative speed of recruitment using these 2 approaches. This secondary analysis is the first to examine these comparisons in a smoking cessation RCT for people experiencing food insecurity, a vulnerable special population for whom quitting is especially urgent.

The delivery of specialist stroke rehabilitation is undergoing a significant transformation, with telerehabilitation increasingly integrated into clinical practice and supported by guidelines and policy. There is a need for the pragmatic evaluation of telerehabilitation in service, which includes insights from clinical teams and people with stroke. This evaluation sought to address that need in the context of community stroke services in the East of England.

Sedentary behavior among older adults is a major public health concern, contributing to the increased risk of chronic diseases and functional decline. With aging populations worldwide, prolonged sitting time (averaging up to 13 h/d in older adults) has been independently associated with cardiovascular disease, metabolic disorders, cognitive decline, and all-cause mortality. Mobile health (mHealth) interventions offer a promising approach to address this issue. However, there remains a lack of evidence-based, systematically developed mHealth programs specifically targeting sedentary behavior in older populations.

Cyberbullying victimization is a significant risk factor for poor psychological well-being among college students. Existing tools fail to capture the distinct dimensions of victimization in social media contexts.

As the population ages, demand for health services among older adults and caregivers increases. Digital health services help meet this demand. However, acceptance and experiences differ between older adults and caregivers.

The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements.

Perinatal depression and anxiety are significant public health concerns, affecting up to 1 in 5 women globally, with disproportionate burden carried by women in regional, rural, and remote communities where structural and social inequities amplify vulnerability. Access to perinatal mental health support in these settings is severely constrained by geographical isolation, workforce shortages, financial barriers, and a lack of culturally safe services. Prevention is recognized as critical to reducing this burden, with evidence suggesting that effective preventive approaches can reduce population-level illness by up to 40% and alleviate downstream demand on overstretched services. Digital mental health interventions hold promise for improving access to support, yet few are co-designed with underserved perinatal populations.

Accurate COVID-19 incidence estimates, including undiagnosed cases, are vital for epidemic management but are often unavailable in real time. Participatory surveillance can capture community illness episodes; however, quantifying undiagnosed infections remains difficult. We assessed a Singaporean cohort to estimate medically unattended COVID-19 infections by combining symptom models with proxy epidemic indicators.

Large language models (LLMs) are becoming increasingly embedded in routine health care communication, raising ethical challenges that extend beyond model performance alone. This Viewpoint argues that ethical risks in LLM-enabled health care emerge through patterns of reliance, institutional embedding, and governance during real-world use. Using “adoption-phase ethics” as an analytic lens, this paper examines 3 interrelated dimensions of ethical risk. First, trust in LLM-enabled health care is shaped not only by technical accuracy, but also by institutional and relational conditions surrounding its use. Second, responsibility may become distributed and ambiguous when LLM-mediated information influences clinical communication and decision-making. Third, equity concerns arise from unequal capacities to interpret, contest, and benefit from LLM-generated information. We argue that ethical governance of LLMs in health care requires continuous, system-level oversight that extends beyond model evaluation alone, including clear accountability structures, role-sensitive implementation, and equity-oriented governance practices. By reframing ethical analysis around routine integration rather than technical performance alone, this Viewpoint aims to support more responsible and sustainable use of LLMs in health care.

Efforts to advance our understanding of depression have long been constrained by the disorder’s vast symptom heterogeneity and by the reliance on self-report, which offers only a partial view of phenotypic expression. Digital phenotyping provides an opportunity to address these core challenges by generating real-time, objective data on behavior and physiology, offering new perspectives on understanding depression phenotypes. Yet, prior efforts to identify such objectively derived subtypes have relied on predefined diagnostic labels or supervised models, limiting discovery to existing clinical categories.