CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
arXiv:2606.31608v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation illusion: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. We introduce CLExEval, a human-in-the-loop framework for evaluating LLM clinical reasoning under progressive information masking. CLExEval combines 5,600 expert-physician annotations with 200 clinical reasoning traces derived from 40 rare diagn
The proliferation of LLMs in specialized domains like medicine necessitates robust and reliable evaluation frameworks to address the critical 'evaluation illusion' issue, particularly as deployment moves closer to reality.
This framework offers a principled approach to evaluating complex AI reasoning, directly addressing a critical bottleneck in the trustworthy application of LLMs in high-stakes fields like clinical medicine.
The ability to accurately and transparently evaluate LLM clinical reasoning moves beyond superficial fluency to deeper understanding and error identification, potentially accelerating safe deployment and adoption.
- · AI developers in healthcare
- · Medical professionals using AI assistants
- · Patients benefiting from more reliable AI diagnostics
- · Academic researchers in AI evaluation
- · LLM developers without robust evaluation methods
- · Companies pushing poorly validated AI solutions
Improved methods for evaluating LLMs will accelerate their safe adoption in sensitive domains like healthcare.
Higher standards for clinical AI reasoning will drive innovation in more robust and explainable LLM architectures.
Successful deployment of ethically validated clinical LLMs could create a precedent for AI integration across other regulated professional sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL