LiveClin: A Live Clinical Benchmark without Leakage

TL;DR

LiveClin, a live benchmark designed for approximating real-world clinical practice, is introduced, using a verified AI-human workflow involving 239 physicians to transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway.

Abstract

The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating real-world clinical practice. Built from contemporary, peer-reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI-human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. The benchmark currently comprises 1,407 case reports and 6,605 questions. Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real-world scenarios, with the top-performing model achieving a Case Accuracy of just 35.7%. In benchmarking against human experts, Chief Physicians achieved the highest accuracy, followed closely by Attending Physicians, with both surpassing most models. LiveClin thus provides a continuously evolving, clinically grounded framework to guide the development of medical LLMs towards closing this gap and achieving greater reliability and real-world utility. Our data and code are publicly available at https://github.com/AQ-MedAI/LiveClin.

Figures (21)

• Figure 1: An example from LiveClin simulating the entire clinical pathway. The case progresses from initial assessment to long-term management, with new clinical information and diverse imaging modalities (e.g., X-ray, MRI, pathology, CT) progressively introduced at each key decision point to challenge the model's reasoning in an evolving scenario.
• Figure 2: LLM Case Accuracy across datasets with different publication dates. Shaded regions indicate the knowledge cutoff for different models
• Figure 3: Overview of the LiveClin Construction Pipeline. Our three-stage pipeline creates a dynamic and clinically authentic benchmark. (1) Case Construction: We source and sample recent, peer-reviewed case reports to build a contemporary data foundation. (2) Exam Generation: An iterative Generator-Critic architecture transforms static reports into sequential reasoning problems. (3) Quality Check: A synergistic AI-human workflow, featuring AI pre-screening and multi-physician verification, ensures factual and logical integrity.
• Figure 4: Distribution of the physician reviewers involved in the quality assurance process. The charts detail the team's composition by clinical specialty and professional rank (Chief Physician, Associate Chief Physician, and Attending Physician) for both the Annotation and Inspection stages.
• Figure 5: Overview of LiveClin's composition and statistics. The top-left panel details the case attrition funnel from construction pipeline and presents summary statistics of the final benchmark. Figure A shows the distribution of question focus across the entire clinical pathway. Figure B displays the distribution by ICD-10 chapters. Figures C and D detail the distribution of image and table modalities, respectively, highlighting the benchmark's multimodal nature.