471-MedHELM: for Real-World Medical Tasks
The paper introduces MedHELM, a comprehensive framework designed to evaluate the performance of large language models across a broad spectrum of medical and operational tasks. Developed through collaboration with clinicians, this suite utilizes 37 diverse benchmarks and a hierarchical taxonomy to assess functions ranging from clinical decision support to administrative workflows. The research highlights that general-purpose AI rankings often fail to predict medical competence, as several top-tier models showed significant performance drops when faced with real-world healthcare data. To ensure…
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