Scoping review of 134 studies on LLM-as-a-Judge in healthcare finds concentration in clinical decision support and NLP, frequent use of OpenAI models with prompt engineering, and moderate-to-strong human alignment where validated.
Verifact: Verifying facts in llm-generated clinical text with electronic health records.arXiv preprint arXiv:2501.16672
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Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
A pre-response classifier predicts user rejection risk for clinical LLM outputs with AUROC 0.719 over 4.5 months of deployment data by incorporating deployment-specific context.
citing papers explorer
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LLM-as-a-Judge in Healthcare: A Scoping Analysis of Applications, Methods, and Human Alignment
Scoping review of 134 studies on LLM-as-a-Judge in healthcare finds concentration in clinical decision support and NLP, frequent use of OpenAI models with prompt engineering, and moderate-to-strong human alignment where validated.
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Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
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Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
A pre-response classifier predicts user rejection risk for clinical LLM outputs with AUROC 0.719 over 4.5 months of deployment data by incorporating deployment-specific context.