JMIR AI

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.

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.

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.

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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.

Spirometry is the standard physiological test defining airflow obstruction, the key criterion for diagnosing chronic obstructive pulmonary disease. It is underused in high-income settings and often unavailable in low- and middle-income countries, causing underdetection. Deep learning analysis of chest radiographs, which are widely available where spirometry is not, may complement spirometric screening, but its use in North American cohorts and across demographic strata has not been examined.

Enhanced recovery after surgery protocols have shortened orthopedic hospital stays but have shifted rehabilitation and safety-monitoring tasks to patients and families after discharge. In this study, AI refers to patient-facing digital systems for orthopedic transitional care, including large language model chatbots, computer vision or platform-based monitoring tools, and wearable sensor–enabled systems for education, rehabilitation guidance, motion correction, and risk alerts. However, patient-reported preferences for different AI-supported functions across the hospital-to-home transition remain underexplored.

AI is increasingly proposed as a tool to enhance disaster medicine through improved situational awareness, decision support, and resource coordination. However, the extent to which current research has progressed beyond methodological development toward integrated, operationally validated systems remains unclear.