JMIR Medical Informatics

Large language models (LLMs) are increasingly used to summarize clinical documents; yet, automated metrics often inadequately capture clinical relevance and safety. In the initial phase of the “Framework and Implementation of AI Tools,” an expert-driven, cocreated evaluation methodology was established to assess LLM-generated discharge letter summaries, combining prompt design considerations with intuitive expert appraisal.

The accurate classification of operative notes is essential for surgical outcomes research; however, CPT code classification is notoriously nonspecific for many procedures. In such situations, the operative note (or “dictation”) must be reviewed manually, a process that is labor-intensive and unsustainable. Natural language processing demonstrates tremendous potential for improving the efficiency and accuracy of procedure classification from unstructured operative notes. To date, it remains unexplored whether natural language processing can reliably differentiate between complex, multicomponent procedures, such as those involved in the care of cleft lip or palate and craniofacial anomalies.

Obstructive sleep apnea (OSA) affects nearly one billion people globally and poses a substantial public health threat. Effective and accessible methods for OSA risk identification are urgently needed.

Retrieval-augmented generation (RAG) systems increasingly support clinical decision-making by grounding large language model outputs in verifiable evidence. The retrieval component is foundational: if the correct document is not retrieved, downstream generation cannot recover it. Despite this, embedding model selection for clinical RAG remains guided by general-domain benchmarks with limited clinical coverage. Given the heterogeneity of clinical documentation across institutions, specialties, and electronic health record systems, it is unclear whether general-domain model rankings generalize to clinical retrieval tasks.

Coughing is a common clinical symptom and a protective respiratory reflex closely associated with various respiratory system diseases. The acoustic characteristics of cough sounds are influenced by underlying pathological factors, with distinct acoustic signatures corresponding to different etiologies. Through rigorous analysis of these sounds, rapid identification and preliminary diagnosis of related conditions may be achieved. This approach holds great potential for broad application in mobile health and ubiquitous health platforms.

The expansion of telehealth services, particularly during the COVID-19 pandemic, has transformed health care delivery in the United States. Telehealth promises greater access and resource efficiency by reducing wait times and appointment lengths, especially in specialties like psychiatry, behavioral health, bariatrics, and sleep medicine. However, disparities exist in adoption based on demographics, geography, and socioeconomic status, raising concerns about equitable access and optimal resource use.

Hearing loss affects approximately 432 million adults globally, with Deaf individuals representing a distinct linguistic and cultural minority that faces significant barriers to accessing health information. These challenges contribute to health disparities by limiting preventive education and timely health interventions.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with frequent exacerbations of COPD (ECOPD) significantly impacting patient health and health care systems. Predicting ECOPD early would increase patients’ quality of life and decrease the economic burden. The advancement of wearable technologies and Internet of Things (IoT) sensors has enabled continuous remote monitoring (RM), offering new opportunities for early ECOPD prediction. However, effectively leveraging wearable data requires robust artificial intelligence (AI) frameworks capable of processing heterogeneous physiological and environmental information.

Medication safety remains a significant challenge in health care, particularly for patients managing complex treatment regimens. In Norway, the introduction of electronic prescribing (e-prescribing) for multidose drug dispensing (eMDD) aims to improve medication adherence and minimize errors by seamlessly integrating with the national e-prescription infrastructure.

Artificial intelligence (AI) scribes using ambient documentation technology that capture clinician-patient dialogue and auto-generate visit notes promise to alleviate documentation burden and reduce clinician burnout. In discussing empirical evidence, highlighting research gaps, and emphasizing technology-related ethical issues beyond established AI and data ethics, we show how this promise comes along with epistemic and relational risks. We proceed in 5 steps: first, we conceptually distinguish ambient documentation from broader ambient intelligence, frame it as a “tech-fix” for documentation-related burnout, and establish the notion of AI scribes as epistemic agents rather than mere transcription tools; second, we summarize empirical evidence on AI scribes, especially with regard to their impact on physicians, highlighting risks such as cognitive deskilling, clinical deprofessionalization, and shifts in epistemic accountability; third, we analyze effects on the patient-physician relationship, focusing on relational and interpretive dimensions, including changes in communication patterns and the omission of narrative nuance; fourth, we highlight risks to patient agency and epistemic justice; and fifth, we propose a design framework for ethical deployment beyond techno-solutionism. We argue that the usefulness of AI scribes should not be justified by short-term effects, but must be assessed in the context of clinical reasoning to improve not only the working conditions of physicians, but also the quality of patient care. The paper proposes a research and design agenda to counter simple “tech-fixes” for systemic problems, envisioning AI scribes that safeguard clinical reasoning and honor patient narratives while delivering relief from documentation burdens.

Predicting enterocutaneous fistula (ECF)–associated sepsis and mortality poses significant challenges in digital health care due to the disease’s complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF.

Most clinically relevant information in emergency department (ED) visits is documented in free text, limiting reuse for research and clinical decision support. Despite growing interest in large language model (LLM)–based feature extraction, very few studies have examined it directly on ED reports. Existing work has mainly addressed binary tasks and rarely evaluated their impact on downstream prediction models. Furthermore, evidence for small multilingual LLMs remains limited, especially for underrepresented languages such as Dutch. Locally deployable LLMs could enable automated feature extraction for decision support systems without increasing physician workload.