JMIR AI

Effective diabetes management requires continuous interpretation of glycemic trends, personalized dietary guidance, and sustained patient education. Although large language models (LLMs) are increasingly being explored for health-related applications, existing general-purpose and biomedical models often struggle with diabetes-specific reasoning and instruction-following, limiting their reliability for domain-focused tasks such as clinical question answering and dietary recommendation tasks.

Motivational interviewing (MI) is widely used in preventive interventions, yet coding MI techniques and monitoring intervention adherence remain resource-intensive due to the reliance on manual transcription and expert review. Large language models (LLMs) offer a promising approach to automate these tasks, but their agreement with human coders in the context of prevention interventions has not been established.

Fairness evaluation is essential for trustworthy clinical risk prediction. However, existing fairness-oriented discrimination metrics either ignore cross-group comparisons or rely on exhaustive pairwise evaluations, making them difficult to interpret and impractical for model selection.

Palliative care is increasingly recognized as essential for an aging population and rising life-limiting illnesses. Machine learning (ML) has been widely applied in this field, primarily for prognostication. However, recent literature suggests broader applications that may enhance patient-centered care and optimize system-level processes.

Multidisciplinary tumor boards (MDTs) constitute the foundation of modern tumor therapy. Large language models (LLMs) are widely discussed for optimizing their recommendations.

Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic complexity. While large language models (LLMs) have shown promise in information retrieval, their utility in the rigorous, end-to-end workflow of guideline maintenance remains underexplored.

Incidental detection of abdominal aortic aneurysms (AAAs) has increased with widespread cross-sectional imaging, while traditional surveillance remains fragmented and clinician-dependent. Real-world descriptions of centralized digital surveillance programs combining structured electronic health record (EHR) queries with natural language processing (NLP) of radiology reports remain limited.

Medication adherence remains a significant concern in both clinical practice and public health. Nonadherence to prescribed medication regimens is associated with poorer health outcomes, higher rates of hospitalization, and increased financial burdens on health care systems. Despite its critical role in ensuring treatment efficacy, adherence assessment is still largely dependent on patient self-reports, pharmacy refill records, or caregiver observations—methods that are often subjective, inconsistent, and unreliable. Therefore, there is an urgent need for objective, automated solutions to accurately monitor medication, particularly the information on the pills taken and intake behavior. AI techniques, especially computer vision, are promising solutions; however, their application in assessing medication adherence has several key pitfalls in maintaining robustness under environmental variation that limit their reliability and real-world applicability.

AI research increasingly depends on energy-intensive computation, yet energy use, greenhouse gas emissions, hardware life cycle burdens, and water consumption are rarely reported in a standardized way. This lack of reproducible environmental accounting limits comparisons across studies and obscures the trade-offs among model performance, infrastructure choices, carbon intensity, and cooling water demand.

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AI-driven clinical systems can improve diagnosis, prognosis, and resource allocation, but they may reproduce disparities encoded in historical health care data. Existing mitigation methods typically target a single source of bias, while clinical datasets often contain interacting representation, proxy, integrity, and temporal biases.

A growing body of literature leverages large language models (LLMs) to make mental health predictions. However, these models are prone to bias, and studies to validate their clinical utility are lacking.