MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline
For medical AI researchers, this benchmark fills a critical gap by testing multi-step evidence integration in realistic guideline generation, showing current LLMs fall short of expert-level requirements.
MedProbeBench introduces the first benchmark for evaluating LLMs on deep evidence integration for clinical guideline development, using 1,200+ rubric criteria and 5,130+ atomic claims. Evaluation of 17 models reveals significant gaps between current capabilities and expert-level performance.
Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medicine, developing clinical guidelines critically depends on such deep evidence integration. However, existing benchmarks fail to evaluate this capability in realistic workflows requiring multi-step evidence integration and expert-level judgment. To address this gap, we introduce MedProbeBench, the first benchmark leveraging high-quality clinical guidelines as expert-level references. Medical guidelines, with their rigorous standards in neutrality and verifiability, represent the pinnacle of medical expertise and pose substantial challenges for deep research agents. For evaluation, we propose MedProbe-Eval, a comprehensive evaluation framework featuring: (1) Holistic Rubrics with 1,200+ task-adaptive rubric criteria for comprehensive quality assessment, and (2) Fine-grained Evidence Verification for rigorous validation of evidence precision, grounded in 5,130+ atomic claims. Evaluation of 17 LLMs and deep research agents reveals critical gaps in evidence integration and guideline generation, underscoring the substantial distance between current capabilities and expert-level clinical guideline development. Project: https://github.com/uni-medical/MedProbeBench