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To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).

To conduct a scoping review of bias assessment in studies applying large language models (LLMs) to health data and to synthesize their prevailing conceptualization of bias.

Physician burnout, often driven by documentation burden in electronic health records (EHRs), remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited.

Dr. Kevin B. Johnson delivered this address on May 16, 2026, at the Commencement Ceremony of The D. Bradley McWilliams School of Biomedical Informatics, UTHealth Houston, to the graduating class of 2026. The address uses the concept of "The Big Mo" (compounding momentum) as a frame for understanding the current inflection point in AI and medicine. Drawing on his own career arc from paper-based clinical practice at Johns Hopkins through [...]

Evaluate whether large language models reason or simply regurgitate training data in clinical diagnosis.

Despite the significant potential for Clinical Decision Support Systems (CDSSs) to improve care processes and health outcomes, several barriers hinder their widespread implementation in healthcare. While numerous systematic reviews have summarized potential barriers and facilitators for CDSS implementation, a comprehensive framework to guide and evaluate the implementation of CDSSs in healthcare is lacking. This overview of reviews, aims to establish a framework-GUIDE-CDSS-aimed at guiding and evaluating implementation of CDSSs in [...]

To understand how hospitals receive and use data on patients' health-related social needs (HRSN) from external sources, and how methods of receipt relate to use.

Objective To develop and validate a targeted set of clinical rules embedded within a clinical decision support system to support safer antimicrobial dosing in patients with impaired renal function and to assist antimicrobial stewardship activities. Methods Clinical rules for renal dose adjustment were developed through a structured literature review and consensus by a multidisciplinary team. The rules were implemented within a clinical decision support system integrated with electronic prescribing, electronic [...]

To examine the evolving role and application of SNOMED CT (SCT) during 2020-2025, a period marked by the COVID-19 pandemic and accelerated adoption of artificial intelligence (AI) in healthcare.

Clarify disciplinary foundations and internal structure of biomedical informatics.