arXiv 2409.189242024-09cs.CLcs.AI

基于大语言模型的AI代理的模拟患者系统有潜力改变医学教育

Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education

The Chinese University of Hong Kong（香港中文大学）；University of Michigan（密歇根大学）；Stanford University（斯坦福大学）；Mass General Brigham（麻省总医院布莱根医疗体系）；Harvard Medical School（哈佛医学院）；Cornell University（康奈尔大学）；Peking University Third Hospital（北京大学第三医院）；Shandong University（山东大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Rutgers University（罗格斯大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）；The University of Hong Kong（香港大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）；Chinese Academy of Medical Sciences（中国医学科学院）；University of Ottawa（渥太华大学）；Peking University Sixth Hospital（北京大学第六医院）

Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, Jack Gallifant, Anye Shi, Xiang Li, Jingxian He, Wenyue Hua, Mingyu Jin, Guang Chen, Yang Zhou, Zhao Li, Trisha Gu… 展开作者

Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, Jack Gallifant, Anye Shi, Xiang Li, Jingxian He, Wenyue Hua, Mingyu Jin, Guang Chen, Yang Zhou, Zhao Li, Trisha Gupte, Ming-Li Chen, Zahra Azizi, Qi Dou, Bryan P. Yan, Yongfeng Zhang, Yanqiu Xing, Themistocles L. Danielle S. Bitterman, Themistocles L. Assimes, Xin Ma, Lin Lu, Lizhou Fan

备注：: 19 pages, 6 figures, 4 tables

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AI总结：

基于大语言模型的AI代理构建的模拟患者系统在医学教育中展现出高保真度和教育价值，优于人类模拟患者。

摘要：

背景：模拟患者系统在医学教育和研究中非常重要，提供安全、整合的训练环境并支持临床决策。人工智能（AI）的进步，特别是大语言模型（LLMs），可以增强模拟患者，通过高保真地复制医疗状况和医生-患者互动来实现低成本的模拟，但有效性和可信度仍存在挑战。方法：我们开发了AIPatient，一个由基于LLM的AI代理驱动的模拟患者系统。该系统使用检索增强生成（RAG）框架，结合六个任务特定的代理进行复杂推理。为了提高真实性，它与从MIMIC III重症监护数据库中提取的去标识化真实患者数据构建的AIPatient知识图谱相连。结果：我们评估了基于电子健康记录（EHR）的医学问答（QA）、可读性、鲁棒性、稳定性及用户体验。AIPatient在所有六个代理启用时达到了94.15%的QA准确率，优于部分或无代理集成的版本。知识库的F1得分为0.89。可读性评分显示中位Flesch阅读易度为68.77，中位Flesch Kincaid等级为6.4，表明对大多数医学培训生和临床医生而言是可及的。鲁棒性和稳定性由重复试验中无显著方差（方差分析F值0.61，p大于0.1；F值0.78，p大于0.1）支持。一项针对医学学生的用户研究显示，AIPatient提供了高保真度、易用性和教育价值，与或优于人类模拟患者在病史采集中的表现。结论：基于LLM的模拟患者系统能够提供准确、可读且可靠的医疗会话，并显示出强烈改变医学教育的潜力。

英文摘要：

Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Advances in artificial intelligence (AI), especially large language models (LLMs), can enhance simulated patients by replicating medical conditions and doctor patient interactions with high fidelity and at low cost, but effectiveness and trustworthiness remain open challenges. Methods: We developed AIPatient, a simulated patient system powered by LLM based AI agents. The system uses a retrieval augmented generation (RAG) framework with six task specific agents for complex reasoning. To improve realism, it is linked to the AIPatient knowledge graph built from de identified real patient data in the MIMIC III intensive care database. Results: We evaluated electronic health record (EHR) based medical question answering (QA), readability, robustness, stability, and user experience. AIPatient reached 94.15 percent QA accuracy when all six agents were enabled, outperforming versions with partial or no agent integration. The knowledge base achieved an F1 score of 0.89. Readability scores showed a median Flesch Reading Ease of 68.77 and a median Flesch Kincaid Grade of 6.4, indicating accessibility for most medical trainees and clinicians. Robustness and stability were supported by non significant variance in repeated trials (analysis of variance F value 0.61, p greater than 0.1; F value 0.78, p greater than 0.1). A user study with medical students showed that AIPatient provides high fidelity, usability, and educational value, comparable to or better than human simulated patients for history taking. Conclusions: LLM based simulated patient systems can deliver accurate, readable, and reliable medical encounters and show strong potential to transform medical education.

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