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Despite rapid integration into clinical decision-making, clinical large language models (LLMs) face substantial translational barriers due to insufficient structural characterization and limited external validation.

To assess patient attitudes towards ambient artificial intelligence (AI) scribes, including comfort, trust, perceived impact on provider interactions, and willingness for future use, and to examine how sociodemographic, health factors, digital literacy, and privacy concerns shape attitudes.

To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center, as well as how ambient transcription reshapes clinicians' perceptions of their work.

Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment.

This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction types: Human-Object Activities (HOA), such as blood pressure measurement, and Human-Human Activities (HHA), such as gland palpation.

Increasingly, structured longitudinal electronic health records (EHRs) are being harnessed to predict risk of having present but as yet undetected disease by analyzing "patient trajectories." Trajectory studies explore clinical event associations, characterize disease trajectories, and enhance risk prediction. This scoping review assesses study characteristics and objectives, identifies model types, and appraises model performance and reporting.

To compare the clinical outcomes of sepsis patients when an augmented systemic inflammatory response syndrome (SIRS+) and the Epic sepsis predictive model version 1 (ESPMv1) alert were active in the emergency department at two county hospitals.

To examine how social media analytics have been applied in research on firearm injury exposure, with a focus on informatics approaches, analytical methodologies, and public health surveillance applications.

Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping review aims to determine the extent of AI research in the rural US.

This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2D), using an analytic framework generalizable to other chronic conditions.