LLMs Can Simulate Standardized Patients via Agent Coevolution
This addresses the problem of scalable and effective medical training for healthcare professionals, representing a novel application of agent coevolution rather than an incremental improvement.
The paper tackles the challenge of training medical personnel by developing EvoPatient, a framework where patient and doctor agents coevolve through multi-turn dialogues to simulate standardized patients, improving requirement alignment by over 10% and achieving better human preference with efficient resource use.
Training medical personnel using standardized patients (SPs) remains a complex challenge, requiring extensive domain expertise and role-specific practice. Previous research on Large Language Model (LLM)-based SPs mostly 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. Our system will be available at https://github.com/ZJUMAI/EvoPatient.