LLM-as-a-Judge in Healthcare Faces Safety and Bias Concerns

A scoping review of Large Language Model-as-a-Judge (LaaJ) applications in healthcare identified significant gaps in validation rigor and safety assessments. The review, which screened over 11,000 studies, found that while LaaJ offers a scalable alternative to expert review, most studies lacked thorough bias testing, human oversight, and temporal stability assessments. To address these issues, the researchers propose the MedJUDGE framework, a three-pillar system designed to guide the evaluation and governance of LaaJ systems in clinical settings. AI

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IMPACT Highlights critical validation and safety gaps in using LLMs for healthcare evaluations, necessitating new governance frameworks like MedJUDGE.

RANK_REASON Academic paper proposing a new framework for evaluating LLM-as-a-Judge systems in healthcare.

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• LLM-as-a-Judge
• MedJUDGE
• PRISMA-ScR
• GPT
• arXiv

• paper
• safety