JMIR AI

Large language models (LLMs) are increasingly deployed in mental health applications, yet growing evidence suggests they encode algorithmic biases that influence clinical outputs. Because these models now mediate patient-facing decisions, such biases carry the potential for direct harm. Whether they systematically affect psychiatric diagnosis across demographic groups remains underexplored.

Health care systems face rising demand and persistent staff shortages, intensifying pressure on the quality and sustainability of care. Artificial intelligence (AI) is increasingly being introduced to improve efficiency and decision support across clinical domains. While these tools promise operational gains, they can also reconfigure how physicians work, make judgments, and interact with patients, all elements of physicians’ craftsmanship. However, most research emphasizes technical performance rather than AI’s broader implications for physicians’ craftsmanship.

Virtual clinical triage is central to digital front door strategies, yet how interaction mode—structured, closed-ended AI questionnaires vs large language model (LLM)–enabled conversational dialogue—affects information capture, engagement, and alignment with care recommendations is unclear.

Vision language models (VLMs) show promise in medical imaging, yet their performance on high-noise smartphone-captured images—common in primary care referrals—remains untested. Furthermore, it remains controversial whether retrieval-augmented generation (RAG) using external expert guidelines actually improves diagnostic accuracy for rare bone tumors.

Type 1 diabetes is characterized by absolute insulin deficiency, requiring exogenous insulin therapy to maintain blood glucose levels within safe ranges. Postprandial glucose control remains particularly challenging, and current meal-related strategies are mainly based on carbohydrate intake. However, other macronutrients, such as fats and proteins, may also influence the magnitude and timing of the glycemic response and are not usually incorporated into glucose forecasting models.

Despite high reported accuracy on clinical and evidence appraisal tasks, AI-generated medical information may lack explicit support from source documents. This creates challenges for digital health practitioners regarding transparency, auditability, and trust when AI systems are used for evidence synthesis, guideline development, and clinical knowledge management. Large language models (LLMs) can generate fluent and seemingly correct outputs, but existing evaluations often rely on agreement with human judgments and do not directly assess whether AI-generated content is grounded in underlying evidence.

Smart home technology powered by AI can detect anomalies and make emergency calls, enabling residents to live safely and independently. However, the adoption of such technologies for medical emergency detection remains limited.

Traumatic brain injury (TBI) is a leading cause of global disability and mortality, requiring timely diagnosis to prevent secondary injury. Manual computed tomographic (CT) evaluation often causes diagnostic delays, especially in smaller hospitals with limited radiological expertise. AI methods have been increasingly proposed to automate CT-based TBI assessment.

Informed consent is a cornerstone of medical ethics, ensuring that patients understand the risks, benefits, and alternatives of procedures before making health care decisions. However, challenges such as complex medical language, time constraints, and variations in patient literacy often hinder comprehension. Recent advancements in AI offer new opportunities to improve the informed consent process.

Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. An AI-based system for mood monitoring (MoodMon) was developed along with a mobile app for smartphones to detect changes in the mental state of individuals with major depressive disorder (MDD) and bipolar disorder (BD) based on acoustic features derived from speech signals. A long-term, naturalistic study of the MoodMon system represents a breakthrough in biomarker validation.

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