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The increasing volume of clinical trial documents presents a significant challenge for biomedical researchers who must analyze vast amounts of unstructured and structured data. Traditional methods are no longer feasible given the scale and complexity of modern clinical trials.

To describe the process of updating the Patient Identification Safety Assurance Factors for EHR Resilience (SAFER) Guide and to review new practices and refinements to the Guide.

Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate a novel computer-assisted reporting tool for clinical decision support during multidisciplinary team meetings (MDTMs) for VS patients.

Generative AI chatbots are revolutionizing health education by making complex information more accessible to the public. However, their use presents risks, including bias, hallucinations, ethical concerns, and misinformation, which are particularly critical in health contexts where incorrect guidance can have serious consequences. Ensuring safety, accuracy, and reliability is essential, especially in maternal and infant health.

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.