JMIR AI

Clinical deterioration in general ward patients is associated with increased morbidity and mortality. Early and appropriate treatments can improve outcomes for such patients. While machine learning tools have proven successful in the early identification of clinical deterioration risk, little work has explored their effectiveness in providing data-driven treatment recommendations to clinicians for high-risk patients.

Advances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connectivity, and overall well-being. Despite these advancements, the elderly population often struggles with barriers such as accessibility, digital literacy, and infrastructure limitations, leaving them at risk of digital exclusion. These challenges underscore the critical need for tailored AI-driven interventions to bridge the digital divide and enhance the inclusion of older adults in the digital ecosystem.

Medical residency is characterized by high stress, long working hours, and demanding schedules, leading to widespread burnout among resident physicians. Although wearable sensors and machine learning (ML) models hold promise for predicting burnout, their lack of clinical explainability often limits their utility in health care settings.

Axial spondyloarthritis (axSpA) is a chronic autoinflammatory disease with heterogeneous clinical features, presenting considerable complexity for sustained patient self-management. Although the use of large language models (LLMs) in health care is rapidly expanding, there has been no rigorous assessment of their capacity to provide axSpA-specific health guidance.

Large language models (LLM) have been shown to answer patient questions in ophthalmology similar to human experts. However, concerns remain regarding their use, particularly related to patient privacy and potential inaccuracies that could compromise patient safety. This study aimed to compare the performance of an LLM in answering frequently asked patient questions about glaucoma with that of a small language model (SLM) trained locally on ophthalmology-specific literature.

HIV viral suppression is essential for improving health outcomes and reducing transmission rates among people living with HIV. In Uganda, where HIV/AIDS is a major public health concern, machine learning (ML) models can predict viral suppression effectively. However, the limited use of explainable artificial intelligence (XAI) methods affects model transparency and clinical utility.

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Objective structured clinical examinations (OSCEs) are widely used for assessing medical student competency, but their evaluation is resource-intensive, requiring trained evaluators to review 15-minute videos. The physical examination (PE) component typically constitutes only a small portion of these recordings; yet, current automated approaches struggle with processing long medical videos due to computational constraints and difficulties maintaining temporal context.

Although Large Language Models (LLMs) show great promises in processing medical text, they are prone to generating incorrect information, commonly referred to as hallucinations. These inaccuracies present a significant risk for clinical applications where precision is critical. Additionally, relying on human experts to review LLM-generated content to ensure accuracy is costly and time-consuming, which sets a barrier against large-scale deployment of LLMs in healthcare settings.

ChatGPT-4o, Google Gemini, and Microsoft Copilot have shown potential in generating health care–related information. However, their accuracy, completeness, and safety for providing drug-related information in Thai contexts remain underexplored.

Mental disorders are frequently evaluated using questionnaires, which have been developed over the past decades for the assessment of different conditions. Despite the rigorous validation of these tools, high levels of content divergence have been reported for questionnaires measuring the same construct of psychopathology. Previous studies that examined the content overlap required manual symptom labeling which is observer-dependent and time-consuming.