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Clinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI Benchmarking
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduced MedQADE, revealing that LLM-as-a-judge evaluators agree with clinicians on answers but fail to replicate critical clinical caution.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 7 October 2026
- Collected by OneBench
- 3 Aug 2026, 09:18 UK
Stored source excerpt
arXiv:2607.01103v2 Announce Type: replace Abstract: Open-response evaluation provides stronger clinical validity than multiple-choice benchmarks but creates a scoring bottleneck that motivates automated LLM-asa-Judge approaches. Whether…
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