LLMs Can Simulate Standardized Patients via Agent Coevolution

• URL: http://arxiv.org/abs/2412.11716v1
• Date: Mon, 16 Dec 2024 12:36:47 GMT
• Title: LLMs Can Simulate Standardized Patients via Agent Coevolution
• Authors: Zhuoyun Du, Lujie Zheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haohao Ying,
• Abstract summary: Training medical personnel using standardized patients (SPs) remains a complex challenge. EvoPatient is a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues. Our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference.
• Score: 8.539733225671059
• License:
• Abstract: Training medical personnel using standardized patients (SPs) remains a complex challenge, requiring extensive domain expertise and role-specific practice. Most research on Large Language Model (LLM)-based simulated patients focuses on improving data retrieval accuracy or adjusting prompts through human feedback. However, this focus has overlooked the critical need for patient agents to learn a standardized presentation pattern that transforms data into human-like patient responses through unsupervised simulations. To address this gap, we propose EvoPatient, a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues, simultaneously gathering experience to improve the quality of both questions and answers, ultimately enabling human doctor training. Extensive experiments on various cases demonstrate that, by providing only overall SP requirements, our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference, while achieving an optimal balance of resource consumption after evolving over 200 cases for 10 hours, with excellent generalizability. The code will be available at https://github.com/ZJUMAI/EvoPatient.

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