JMIR AI

Health promotion aims to strengthen individuals’ and communities’ capacity to maintain health and well-being through behavior change, empowerment, and supportive environments. Achieving this requires interventions that are timely, personalized, and scalable—qualities increasingly supported by artificial intelligence (AI). However, research on AI-enabled health promotion remains fragmented, organized primarily around technological labels rather than the intervention purposes these tools serve, limiting the cumulative understanding of how AI techniques are applied across health promotion contexts.

Artificial intelligence (AI) is increasingly being integrated into health care to streamline documentation and improve clinician efficiency. AI-powered documentation tools, such as CarePilot, may reduce administrative burdens and help mitigate burnout. However, their usability and perceived value among medical trainees remain underexplored.

With the rapidly aging population, mental health among older adults has received growing attention. Although the likelihood of experiencing depressive symptoms is higher in late adulthood, older adults are more reluctant to visit a clinic due to the stigma surrounding mental health issues, and many remain undiagnosed and untreated. Digital phenotyping has emerged as a promising approach to mitigate this problem. Longitudinal monitoring via wearable devices can facilitate the timely identification of depressive symptoms in older adults. However, there has not been sufficient investigation to develop a machine learning approach that accounts for between-person and within-person characteristics.

In occupational therapy, progress notes and other client-related administrative tasks are essential for providing treatment but are time-consuming. Therapists spend at least as much time on these tasks as providing care, which contributes to growing waitlists.

Clinical notes are the most abundant data type within electronic health records; however, their highly unstructured format presents significant challenges for supervised natural language processing (NLP) methods. The NLP community is increasingly adapting large language models to analyze clinical notes, achieving strong performance and generalizability with minimal task-specific fine-tuning. We conducted a scoping review of NLP methods applied to clinical notes prior to widespread adoption of generative artificial intelligence (AI) to establish a pre–large language model methodological baseline, showcase potential clinical utility, and highlight key challenges and limitations of extractive, supervised techniques that generative AI approaches may need to overcome.

Effective communication about clinical trials is essential, as low enrollment undermines scientific validity and contributes to health care inequities. However, recruitment remains a persistent challenge, particularly among older adults, minority populations, and individuals with limited health literacy. Although large language models (LLMs) show promise in understanding and generating health information, it is unclear whether these generative artificial intelligence (AI) tools can improve the content of hospitals’ frequently asked questions (FAQ) pages to enhance public attitudes and intentions toward clinical trial participation.

Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detection are critical to mitigating its impact. Automated machine learning (AutoML) models have the potential to predict individual risk and guide personalized interventions. However, their clinical deployment remains limited due to the retrospective nature of most datasets, a lack of external validation, and heterogeneity in variable selection.

Regulation of artificial intelligence (AI) has been slow relative to the pace of its integration into health care. Several AI diagnostic tools for diabetic retinopathy (DR) have already received Food and Drug Administration (FDA) clearance, making it a timely and concrete example for exploring public perspectives on regulatory approval. The scope of FDA regulation of AI tools is being explored, and public attitudes about regulatory oversight should inform these discussions and are explored in this paper. Prior research suggests that comfort, trust, and political orientation shape views on government regulation and emerging technologies, potentially affecting support for oversight of AI in health care.

Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal dynamics, and care processes in complex health care systems requires processing vast amounts of data that capture fluid interactions among professionals—a task that has been cumbersome, costly, and avoided by most organizations. Well-designed artificial intelligence (AI) tools can meaningfully advance the frontier of health care teamwork, but the application of AI in this regard has been lagging. To support this development, we outline the potential for AI to help optimize team composition, strengthen norms and relationships among professionals, and standardize team-based clinical care processes. These applications can improve the integration of health care teams. Given the importance of relevant data for realizing such advances, we describe the potential types and sources of data that can support AI development. Furthermore, we highlight enabling strategies, including data-sharing alliances and leadership engagement to address privacy, interoperability, and ethical considerations. We propose a sequenced roadmap for piloting these applications based on technological readiness and clinical feasibility, ensuring that human oversight remains central as AI tools are introduced into complex care environments.

Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis and underdiagnosis are common, and older adults may be at particular risk of misdiagnosis. Artificial intelligence (AI) shows promise in augmenting diagnosis, but performance across patient populations remains underexamined.

Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must balance accuracy, relevance, and safety, as older adults may be particularly susceptible to misleading or harmful advice. However, systematic evaluations of expert perceptions across multiple geriatric conditions remain limited.

The integration of Large Language and Vision Assistant models with food and nutrition data enables multimodal meal analysis and contextual dietary guidance. Despite this potential, the reliability and practical usefulness of such systems for supporting everyday dietary decision-making remain underexplored.