Abstract Clinical artificial intelligence (AI) has advanced rapidly, with frontier large language models now matching or exceeding physician performance on simulated diagnostic reasoning and clinical decision-support tasks. Yet adoption has outpaced the evidence base: fewer than 5% of cleared U.S. Food and Drug Adminis...
J. Worth, Anastasia Perez, David Wu et al.· BMJ digital health & AI· 1 citation
A scalable framework that automatically generates question--answer pairs from longitudinal EHR notes that supports evaluations that static benchmarks cannot, including the generation of multiple answers that reflect variation in clinician reasoning for robust performance assessment and continuously refreshing benchmark...
J. Cahoon, C. Stanwyck, Sulaiman Somani et al.· 1 citation
Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and utility depend on rigorous evaluation, yet existing benchmarks are manually curated,...
J. Cahoon, C. Stanwyck, Sulaiman Somani et al.· 0 citations
This article aims to provide a pragmatic basis for designing and executing rigorous evaluations of healthcare LLMs by describing underlying concepts and potential pitfalls, while emphasizing the importance of aligning evaluation methods with the research question.
S. E. McKinney, P. Vu, S. Justice et al.· 0 citations
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