通过外部规划器控制的大语言模型实现对话式疾病诊断
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
- Zhejiang Lab（浙江大学实验室）
- Transtek Medical Electronic（深圳迈迪特医疗电子）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对大语言模型不擅长主动收集患者数据的问题，本研究提出含两个外部规划器的LLM诊断系统，分别用强化学习做筛查初诊、用LLM解析指南做鉴别诊断，在真实病历模拟对话中表现优异，推动AI融入临床。
AI中文摘要：
大语言模型（LLMs）的发展为基于人工智能（AI）的医疗诊断带来了前所未有的可能性。然而，由于LLMs不擅长主动收集患者数据，其在真实诊断场景中的应用前景仍不明朗。本研究提出了一种基于LLM的诊断系统，通过模拟医生来增强规划能力。我们的系统包含两个处理规划任务的外部规划器：第一个规划器采用强化学习方法制定疾病筛查问题并进行初步诊断；第二个规划器利用LLMs解析医疗指南并开展鉴别诊断。我们利用真实患者的电子病历数据构建了虚拟患者与医生之间的模拟对话，以此评估系统的诊断能力。实验证明，我们的系统在疾病筛查和鉴别诊断两项任务中均取得了出色的性能。本研究向着更顺畅地将AI融入临床场景迈出了一步，有望提升医疗诊断的准确性和可及性。
英文摘要：
The development of large language models (LLMs) has brought unprecedented possibilities for artificial intelligence (AI) based medical diagnosis. However, the application perspective of LLMs in real diagnostic scenarios is still unclear because they are not adept at collecting patient data proactively. This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors. Our system involves two external planners to handle planning tasks. The first planner employs a reinforcement learning approach to formulate disease screening questions and conduct initial diagnoses. The second planner uses LLMs to parse medical guidelines and conduct differential diagnoses. By utilizing real patient electronic medical record data, we constructed simulated dialogues between virtual patients and doctors and evaluated the diagnostic abilities of our system. We demonstrated that our system obtained impressive performance in both disease screening and differential diagnoses tasks. This research represents a step towards more seamlessly integrating AI into clinical settings, potentially enhancing the accuracy and accessibility of medical diagnostics.
