The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accuracy, safety, and ethical alignment. Existing frameworks inadequately dissect domain-specific error patterns or address cross-modal challenges. This study introduces a granular error taxonomy through systematic analysis of top 10 models on MedBench, categorizing incorrect responses into eight types: Omissions, Hallucination, Format Mismatch, Causal Reasoning Deficiency, Contextual Inconsistency, Unanswered, Output Error, and Deficiency in Medical Language Generation. Evaluation of 10 leading models reveals vulnerabilities: despite achieving 0.86 accuracy in medical knowledge recall, critical reasoning tasks show 96.3% omission, while safety ethics evaluations expose alarming inconsistency (robustness score: 0.79) under option shuffled. Our analysis uncovers systemic weaknesses in knowledge boundary enforcement and multi-step reasoning. To address these, we propose a tiered optimization strategy spanning four levels, from prompt engineering and knowledge-augmented retrieval to hybrid neuro-symbolic architectures and causal reasoning frameworks. This work establishes an actionable roadmap for developing clinically robust LLMs while redefining evaluation paradigms through error-driven insights, ultimately advancing the safety and trustworthiness of AI in high-stakes medical environments.

本研究针对当前医学大型语言模型在准确性、安全性和伦理一致性方面的不足，提出了一种细致的错误分类法，并分析了前10个模型在MedBench上的表现。通过识别错误类型并揭示症结所在，研究提出了四级优化策略，旨在开发更具临床应用价值的医学LLMs，进而提升AI在高风险医疗环境中的安全性与可信度。

基于Medbench的中国医学大型语言模型基准测试：性能差距与分层优化策略分析