JMIR AI

Quality-of-life (QoL) questionnaires are an established instrument designed to assess overall well-being and QoL of patients. They are important in predicting the outcome of the disease and understanding the needs of individual patients. However, their repeated collection imposes a substantial burden on both patients and clinical professionals. Many patients seek emotional support and mutual exchange in online communities for peer support, where they frequently share detailed descriptions of symptoms and treatment experiences, addressing topics covered in QoL questionnaires. The emergence of large language models (LLMs) uncovers potential for automatic extraction of relevant QoL information from patient-generated text.

Conversational AI systems are increasingly being deployed in health care for clinical decision support, but their performance varies substantially across patient communication styles, health literacy levels, and behavioral patterns. Static benchmarks cannot capture multiturn dynamics through which this variation compounds, and no current evaluation framework implements structured AI risk management guidance for conversational health care AI. The result is a structural risk: AI systems may perform well in aggregate while failing disproportionately for the populations they are intended to help.

Oral disease and malnutrition are common and closely linked problems in long-term care (LTC). Monthly weights, occasional diet reviews, and infrequent dental assessments can miss gradual decline. Recent tools, including computer-vision meal-intake estimation and smartphone-based gingival screening, create an opportunity for more timely clinical monitoring when limited data capture is embedded into routine care. This viewpoint proposes a nursing-led policy framework for AI-enabled oral and nutrition risk detection in nursing homes. The framework emphasizes protected oral health and nutrition champion roles, a practical 48-hour bedside assessment standard for high-priority operational alerts, standards-based electronic health record (EHR) integration, and prevention-oriented escalation pathways. We clarify that the proposed approach does not require a single black-box AI risk score. Instead, AI-derived measurements, such as estimated intake, plate-waste ratio, deviation from baseline, and image-based oral findings, can be combined with weight trends, EHR data, operational thresholds, and nurse review. The playbook specifies staged rollout, staff-facing alert outputs, fidelity checks for data capture, fallback documentation options for facilities with lower digital maturity, and key performance indicators for clinical outcomes, workflow burden, equity, and cost. Ethical safeguards include layered consent, minimum-necessary capture, opt-out recording, explainability for residents and proxies, and subgroup monitoring. AI-enabled clinical monitoring can support earlier action in LTC only if it is embedded in nursing workflows, auditable documentation, and accountable governance. Prospective, co-designed implementation studies are needed to test feasibility, workload, effectiveness, and equity across diverse LTC settings.

Human-autonomy teaming (HAT) has the potential to reshape surgical practice by fostering true partnership between surgeons and intelligent systems. To achieve this, AI must move beyond static scoring to provide adaptive, real-time guidance based on reliable skill assessment.

AI is already in the mental health consulting room, whether clinicians invite it or not. OpenAI reports more than 800 million regular users of ChatGPT and more than 40 million people turning to the platform daily for health questions, yet the evidence remains limited on the clinical efficacy of large language model–based mental health chatbots. Professional guidance and regulation remain fragmented. Clinicians are therefore practicing in a gap between a patient reality that cannot be ignored and a professional infrastructure not yet built. We argue that neither enthusiastic adoption nor principled abstention is sufficient in the current environment. The clinician who refuses to discuss clinically material patient AI use is not preventing that use; they may lose visibility into a factor materially affecting the therapeutic process. The clinician who adopts AI without structured evaluation exposes patients and practice to avoidable harm. The stance we propose is structured harm reduction within the therapeutic relationship: inviting discussion at intake, integrating material use into case formulation, discussing benefits and limits, establishing crisis boundaries, monitoring throughout treatment, and addressing termination planning. Clinician-initiated use is a separate choice requiring evidence, consent when applicable, privacy safeguards, and accountability. We propose a phased clinical framework spanning preintake to termination anchored by the gather, understand, inform, document, and evaluate (GUIDE) mnemonic as an operational wrapper for daily practice. GUIDE and the phased framework are conceptual, unvalidated proposals. We distinguish ethical recommendations, established legal duties, and predicted standards; scope the legal discussion to the United States; integrate equity and subgroup performance; and offer proportionate implementation. The patients who seek care in an AI-saturated world deserve professional infrastructure capable of holding innovation and patient protection in informed balance.

AI systems are increasingly deployed across National Health Service (NHS) services, yet safety and implementation challenges may only become apparent after clinical go-live. Existing governance and implementation frameworks provide valuable high-level guidance, but health care provider organizations still require practical, auditable tools to support preimplementation decision-making.

Antiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning (ML) approaches are increasingly being explored to support personalized ASM selection, but successful implementation will depend on acceptance by both people with epilepsy and treating neurologists. Understanding factors influencing acceptability of ML in clinical decision-making is therefore critical to support engagement, trust, and adherence. The extended unified theory of acceptance and use of technology (UTAUT2) framework has previously been applied to evaluate acceptance of health care technologies, but its suitability for ML-supported ASM selection has not been established.

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.