JMIR Medical Informatics

Primary care is becoming increasingly complex, with primary care physicians (PCPs) facing rising workloads driven by workforce shortages, growing administrative demands, and expanding clinical responsibilities. Recent advances in large language models (LLMs) offer new opportunities to support PCPs across clinical, administrative, and communication-related tasks within their workflows. Understanding how these technologies are perceived and used in primary care practice is, therefore, critical to inform their safe, effective, and human-centered implementation.

Cardiovascular diseases (CVDs) and type 2 diabetes (DM2) are influenced not only by biomedical risk factors but also by social determinants of health (SDOH). While the inclusion of SDOH in predictive models is increasingly advocated, few studies have quantified their specific contribution in a high-risk clinical cohort using robust statistical and machine-learning approaches.

Supraspinatus tendon pathologies are common causes of shoulder pain. Magnetic resonance imaging (MRI) is the reference imaging method but requires expert interpretation. Automated classification may improve diagnostic consistency and support musculoskeletal imaging workflows.

A 2019 French national online survey explored activity limitations and participation restrictions among children and youth with disabilities for which innovative solutions could help.

Although individually uncommon, rare diseases (RDs) collectively affect an estimated 329-624 million people worldwide. There are over 6500 known RDs, 85% of which affect fewer than 1 person per million. Consequently, the critical amount of data necessary to improve knowledge, care, and treatment can only be achieved through cumulative data collection across countries. However, RDs remain underrepresented in medical terminologies and classification systems, hindering data sharing, interoperability, and public health monitoring.

The increasing documentation burden on physicians is a significant contributor to burnout and decreases in care quality. Artificial intelligence (AI) has been proposed as a solution to reduce documentation burden in clinical care, but there are very limited data on its use in the inpatient and intensive care unit (ICU) environments.

Patients with cirrhosis may have acute kidney injury (AKI) after receiving endoscopic retrograde cholangiopancreatography (ERCP).

Qualitative thematic analysis is widely used in health research to examine patient experiences and inform the refinement of digital health interventions, but it is time- and labor-intensive. Large language models (LLMs) may help accelerate this process, yet their performance may depend not only on the model itself but also on how the analytic workflow is structured. Current evidence remains limited on how different LLMs perform across multistage thematic analysis workflows and across multiple health-related qualitative datasets.

Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses problems such as unequal sampling time intervals and frequencies, making it difficult to align the data without losing information for input into models. Meanwhile, due to information loss in the dataset, it is also difficult for the model to effectively analyze the overall data variation and predict the complex and dynamic conditions of emergency patients in the future.

Computer-aided diagnostic systems such as S-Detect (Samsung Medison) are increasingly integrated into breast ultrasound workflows. Notwithstanding extensive past evaluation of S-Detect’s diagnostic accuracy, its intrasystem repeatability at the software level with identical static images, a fundamental prerequisite for clinical reliability, has not been systematically investigated.

Concept mapping (CM) is a widely used mixed method research approach for structuring and visualizing complex ideas across various fields, such as the health sciences. A critical bottleneck in the CM process is the idea synthesis phase, which remains labor-intensive, subjective, and consequently challenging to scale for large datasets.

In intensive care unit (ICU) settings, structured team-based communication, such as multidisciplinary rounds, handoffs, and goals-of-care discussions, is foundational to high-quality care. However, accurately documenting these complex discussions in the medical record remains a challenge due to time pressures, documentation burdens, and competing clinical demands. Ambient artificial intelligence (AI) scribes, which passively transcribe and summarize spoken interactions, offer a potential solution to assist ICU clinicians with documentation. Yet, little is known about how ICU clinicians perceive the integration of these tools into their high-stakes, collaborative workflows.