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

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.