JMIR Medical Informatics

Frailty is prevalent among hospitalized patients and is associated with increased risk of complications, prolonged hospitalization, higher mortality, and health care costs. Existing frailty screening tools, including the Clinical Frailty Scale (CFS) and Hospital Frailty Risk Score (HFRS), have limitations related to subjectivity, resource intensity, or reliance on retrospective data, limiting their use in routine acute care. Early identification of frailty is critical to support targeted interventions and resource allocation, yet scalable approaches for predicting frailty early during hospital admission remain limited.

Schizophrenia is a severe mental disorder characterized by high disability and relapse rates, posing significant challenges to public safety and community-based mental health management.

The rapid rise of secure patient messaging has transformed ophthalmic communication, with text volumes now exceeding prepandemic levels across health care systems. Our study uses a natural language processing (NLP) model to explore patterns in over 30,000 secure messages and identify common ocular complaints and differences by patient demographics.

Large language models (LLMs) are increasingly deployed as conversational agents for communication and guidance, but their ability to adapt responses to a patient’s emotional state and literacy level remains insufficiently characterized. In patient-facing scenarios, effective communication requires not only accurate medical content but also appropriate tone, clarity, and responsiveness to user vulnerability.

Medical record quality is a key focus of health care quality management. Traditional manual medical record quality control (QC) suffers from low efficiency, inability to achieve full-sample coverage, and poor consistency. Existing intelligent QC systems have significant deficiencies in real-time intervention and quantitative assessment of application effects.

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The Functional Independence Measure (FIM) is a standard functional assessment tool in rehabilitation medicine; however, formal assessments are often conducted only at limited intervals. Free-text clinical notes contain more frequent observations of patients’ functional status and may complement these assessments.

Ambient AI scribes have been rapidly adopted across health care organizations to mitigate documentation burden and burnout. Initial single-system reports described substantial time savings, but more recent multisite evaluations and meta-analyses describe modest, heterogeneous effects with generally low methodological quality. Most evaluations emphasize efficiency and satisfaction, leaving potential effects on communication quality, narrative preservation, cognitive performance, and equity largely unmeasured. This viewpoint aimed to characterize evaluation patterns in prominent ambient AI scribe implementations, explain why unmeasured domains warrant investigation for patient safety and care quality, and propose an expanded framework with feasible research priorities. This analytic viewpoint mapped evaluation domain coverage in a purposive, illustrative sample of prominent implementation reports published through April 2026, then synthesized findings with literature on clinical communication, narrative medicine, automation effects, and health equity. Reporting was guided by Scale for the Assessment of Narrative Review Articles (SANRA) principles for narrative reviews; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was not used because the work is a domain-coverage characterization rather than an evidence synthesis estimating effects. A single reviewer (VSME) performed selection and extraction, consistent with the viewpoint’s illustrative intent. Across the included implementations, evaluation consistently measured documentation time, after-hours work, and clinician satisfaction. Within this illustrative sample of 6 independently peer-reviewed real-world implementation evaluations, we did not identify any that reported systematic assessment of patient-clinician communication using validated instruments, structured comparison of AI-generated against human-generated notes for narrative fidelity, longitudinal evaluation of cognitive or educational effects, or equity analyses stratified by language and demographic group. The absence in this purposive sample reflects an absence of public reporting rather than proof that no organization has measured these dimensions internally. Limited error analyses found frequent omissions, with accuracy declining as encounter complexity increased. Evidence from multilingual and interpreter-mediated encounters was notably sparse. Current evidence supports continued implementation with quality safeguards in settings where benefits are demonstrated locally rather than universal adoption on the strength of early single-system reports. Organizations implementing ambient AI scribes should consider embedding prospective evaluation across 4 domains: validated communication quality measurement, structured narrative content comparison, longitudinal cognitive and training studies, and equity analyses across languages and demographics. Three near-term priorities offer immediate feasibility for organizations with evaluation capacity: (1) deploying validated communication instruments such as the Communication Assessment Tool or Consumer Assessment of Health Care Providers and Systems (CAHPS) within ongoing rollouts; (2) launching multisite longitudinal trainee cohorts comparing programs with and without ambient AI exposure; and (3) oversampling multilingual and interpreter-mediated encounters for stratified equity analyses. Narrative preservation requires foundational methodological work before implementation-ready measures exist. Editors, implementers, and vendors can each act now: editors by requiring domain-coverage reporting; implementers by preregistering evaluation plans before rollout; vendors by publishing stratified performance data as a routine condition of deployment. The goal is to preserve documented efficiency gains while generating evidence about communication, narrative, reasoning, and equity dimensions that current evaluations leave unmeasured.

Publicly accessible, internet-based AI chatbots have emerged as unregulated digital health resources that clinicians and patients consult in real time, including during acute medical emergencies. In dental practice—where life-threatening events such as anaphylaxis, cardiac arrest, and severe hypoglycemia can unfold within seconds—the unsupervised use of these tools as point-of-care decision aids poses uncharacterized patient safety risks. Despite widespread deployment, no study has systematically evaluated the accuracy, temporal consistency, or multilingual reliability of contemporary large language model (LLM)–based chatbots specifically for dental medical emergency management.

With the rapid expansion of health care data, multiple international standardized terminology systems have been developed and refined to ensure semantic interoperability, which is a crucial factor in facilitating collaborative use.

Early-stage pulmonary sarcoidosis with serum angiotensin-converting enzyme (sACE) activity within the normal range is difficult to distinguish from other pulmonary diseases with similar radiographic characteristics.

Acute kidney injury (AKI) is a common, life-threatening complication among hospitalized patients. Timely prediction is crucial for prevention, while accurate prognosis following onset is essential for guiding care. Although electronic health records (EHRs) provide rich longitudinal data, their high dimensionality, heterogeneity, and temporal complexity pose challenges for traditional machine learning.