Clinical Language Model Evaluation
Overview
Clinical language model evaluation asks whether models can answer medical questions, interpret records, support conversations, and assist with decisions without causing harm. Researchers use exam-style benchmarks, clinician-authored rubrics, electronic health records, simulated consultations, and tests that vary language, missing information, patient behavior, or evidence; they also compare expert ratings with automated judges. Evaluation increasingly measures safety, omissions, evidence grounding, uncertainty, consistency, and workflow performance alongside accuracy. A central challenge is that strong scores on static tests or plausible reasoning may not transfer to dynamic, incomplete clinical encounters, while automated judges can disagree with clinicians and require validation.
Latest papers
419 papers49 papers in the last four weeks, up 2% on the four weeks before. 0.3% of all new papers.