让医疗大模型自动纠错，逐步提升诊断准确率。
Adaptive Reasoning and Acting in Medical Language Agents
- 设计可自适应纠错的医生代理，通过与模拟患者动态交互优化决策。
- 在AgentClinic基准上实现正确诊断，展现持续改进能力。
- 适合研究医疗AI决策、自适应系统的人参考。
本文提出一种创新的大语言模型（LLM）代理框架，用于在模拟临床环境中提升诊断准确性，基于AgentClinic基准测试。所提出的自动纠错机制使医生代理在诊断错误后能够迭代优化其推理与行动，从而逐步改善决策质量。实验表明，采用自适应的LLM医生代理可通过与模拟患者的动态交互实现正确诊断。评估结果凸显了自主代理在复杂医疗场景中自我适应与进化的潜力。未来工作将聚焦于算法优化及扩展至更广泛任务和不同大语言模型的应用。
原文摘要 · Abstract (English)
This paper presents an innovative large language model (LLM) agent framework for enhancing diagnostic accuracy in simulated clinical environments using the AgentClinic benchmark. The proposed automatic correction enables doctor agents to iteratively refine their reasoning and actions following incorrect diagnoses, fostering improved decision-making over time. Experiments show that the implementation of the adaptive LLM-based doctor agents achieve correct diagnoses through dynamic interactions with simulated patients. The evaluations highlight the capacity of autonomous agents to adapt and improve in complex medical scenarios. Future enhancements will focus on refining the algorithm and expanding its applicability across a wider range of tasks and different large language models.
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