BMC medical informatics and decision making
Dec 15, 2025
BACKGROUND: Early identification of septic patients who will require vasopressor support could provide a critical window for hemodynamic optimisation, yet current bedside cues often appear only when shock is imminent.
BACKGROUND: While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, US health care providers and hospital administrators have increasingly embraced ML, particularly through its integration...
BACKGROUND: Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time-known as dataset shifts, which i...
BACKGROUND: Medical feature extraction from clinical text is challenging because of limited data availability, variability in medical terminology, and the critical need for trustworthy outputs. Large language models (LLMs) offer promising capabilitie...
Digital health is expected to improve the efficiency and quality of health. Health information technologies (HIT) imply allocated time, appropriate training, and new types of responsibility, whose physical and mental impact on health care professiona...
BACKGROUND: Many institutions are in various stages of deploying an artificial intelligence (AI) scribe system for clinic electronic health record (EHR) documentation. In anticipation of the University of California, Davis Health's deployment of an A...
PURPOSE OF REVIEW: Accurate, timely quantification of cardiac function is central to the diagnosis, management, and monitoring of cardiovascular disease. This review synthesizes recent advances in artificial intelligence (AI) applications across the ...
BACKGROUND: Integrating digital technologies in clinical dentistry training is increasingly recognized as essential for modernizing competency-based education. However, structured frameworks for digital technology adoption in dental training are lack...
Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtypi...
BACKGROUND: Electronic health records (EHRs) contain comprehensive information regarding diagnoses, clinical procedures, and prescribed medications. This makes them a valuable resource for developing automated hypertension medication recommendation s...
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