LiveClin: A Live Clinical Benchmark without Leakage
arXiv:2602.16747v1 Announce Type: new 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
arXiv:2602.16747v1 Announce Type: new 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.
Executive Summary
The article 'LiveClin: A Live Clinical Benchmark without Leakage' addresses the critical issues of data contamination and knowledge obsolescence in evaluating medical large language models (LLMs). The authors introduce LiveClin, a dynamic benchmark that uses contemporary, peer-reviewed case reports updated biannually to ensure clinical relevance and resist data leakage. The benchmark, developed with input from 239 physicians, includes 1,407 case reports and 6,605 questions, covering the entire clinical pathway. Evaluation of 26 models on LiveClin shows that the top-performing model achieved only 35.7% Case Accuracy, highlighting the complexity of real-world clinical scenarios. Human experts, particularly Chief Physicians and Attending Physicians, outperformed most models, underscoring the need for improved medical LLM development.
Key Points
- ▸ Introduction of LiveClin, a live clinical benchmark designed to address data contamination and knowledge obsolescence.
- ▸ Use of contemporary, peer-reviewed case reports updated biannually to ensure clinical currency.
- ▸ Evaluation of 26 models on LiveClin reveals significant difficulty, with top model achieving 35.7% Case Accuracy.
- ▸ Human experts, particularly Chief Physicians and Attending Physicians, outperformed most models.
- ▸ LiveClin provides a continuously evolving framework for developing reliable and useful medical LLMs.
Merits
Innovative Approach
LiveClin's use of a live benchmark that is regularly updated with contemporary case reports is a significant innovation in the field of medical LLM evaluation. This approach ensures that the benchmark remains relevant and resistant to data contamination.
Comprehensive Evaluation
The benchmark's inclusion of 1,407 case reports and 6,605 questions, covering the entire clinical pathway, provides a comprehensive evaluation of medical LLMs, making it a robust tool for assessing model performance.
Human Expert Involvement
The involvement of 239 physicians in developing the benchmark ensures that the scenarios are authentic and complex, closely approximating real-world clinical practice.
Demerits
Limited Model Performance
The relatively low performance of the top model (35.7% Case Accuracy) indicates that current medical LLMs are still far from achieving reliable real-world utility, highlighting the need for further development.
Potential Bias
The benchmark's reliance on human experts for scenario development and evaluation may introduce biases, which could affect the objectivity and generalizability of the results.
Resource Intensive
The process of regularly updating the benchmark with new case reports and involving a large number of physicians is resource-intensive, which could limit its scalability and widespread adoption.
Expert Commentary
The introduction of LiveClin represents a significant advancement in the evaluation of medical LLMs. By addressing the critical issues of data contamination and knowledge obsolescence, LiveClin provides a robust and dynamic framework for assessing model performance. The involvement of human experts ensures that the benchmark scenarios are authentic and complex, closely approximating real-world clinical practice. However, the relatively low performance of the top model highlights the need for further development in medical LLMs. The resource-intensive nature of LiveClin's approach may limit its scalability, but the benefits of a continuously evolving benchmark are substantial. Policymakers and practitioners should consider the implications of LiveClin's findings for the ethical and responsible use of AI in medical applications, as well as the need for regulatory frameworks that support the development and adoption of dynamic benchmarks.
Recommendations
- ✓ Further development and refinement of medical LLMs to improve their performance on benchmarks like LiveClin.
- ✓ Exploration of methods to reduce the resource intensity of dynamic benchmarks, such as LiveClin, to enhance their scalability and widespread adoption.
