Simulated patient systems are intelligent when powered by large language model-based AI agents

• URL: http://arxiv.org/abs/2409.18924v3
• Date: Tue, 29 Jul 2025 06:10:45 GMT
• Title: Simulated patient systems are intelligent when powered by large language model-based AI agents
• Authors: 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, Yongfeng Zhang, Yanqiu Xing, Themistocles L. Danielle S. Bitterman, Themistocles L. Assimes, Xin Ma, Lin Lu, Lizhou Fan,
• Abstract summary: We developed AIPatient, an intelligent simulated patient system powered by large language model-based AI agents.<n>The system incorporates the Retrieval Augmented Generation framework, powered by six task-specific LLM-based AI agents for complex reasoning.<n>For simulation reality, the system is also powered by the AIPatient KG (Knowledge Graph), built with de-identified real patient data.
• Score: 32.73072809937573
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Simulated patient systems play an important role in modern medical education and research, providing safe, integrative medical training environments and supporting clinical decision-making simulations. We developed AIPatient, an intelligent simulated patient system powered by large language model-based AI agents. The system incorporates the Retrieval Augmented Generation (RAG) framework, powered by six task-specific LLM-based AI agents for complex reasoning. For simulation reality, the system is also powered by the AIPatient KG (Knowledge Graph), built with de-identified real patient data from the Medical Information Mart for Intensive Care (MIMIC)-III database. Primary outcomes showcase the system's intelligence, including the system's accuracy in Electronic Record (EHR)-based medical Question Answering (QA), readability, robustness, and stability. The system achieved a QA accuracy of 94.15% when all six AI agents present, surpassing benchmarks with partial or no agent integration. Its knowledgebase demonstrated high validity (F1 score=0.89). Readability scores showed median Flesch Reading Ease at 77.23 and median Flesch Kincaid Grade at 5.6, indicating accessibility to all medical professionals. Robustness and stability were confirmed with non-significant variance (ANOVA F-value=0.6126, p > 0.1; F-value=0.782, p > 0.1). A user study with medical students further demonstrated that AIPatient offers high fidelity, strong usability, and effective educational value, performing comparably or better than human-simulated patients in medical history-taking scenarios. The promising intelligence of the AIPatient system highlights its potential to support a wide range of applications, including medical education, model evaluation, and system integration.

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