aceSimulated patient systems powered by large language model-based AI agents offer potential for transforming medical education
BACKGROUND: Simulated patient systems are vital in medical education and research, providing safe, integrative training environments and supporting clinical decision-making. Progressive Artificial Intelligence (AI) technologies, such as Large Language Models (LLM), could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, effectiveness and trustworthiness remain challenging. METHODS: We developed AIPatient, a simulated pati…
AIPatient simulated patient system using six LLM agents and a knowledge graph built from MIMIC-III achieved 94.15% EHR-based QA accuracy and accessible readability, with medical students rating it high fidelity and matching or exceeding human-simulated patients for history-taking.
Evidence
- Peer-reviewedCommunications Medicine2025-12-19
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Truvace Impact Record TRV-2026-0427, v1: “Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education.” Truvace, 2026-07-20. /record/TRV-2026-0427 (accessed at citation time). sha256 f28b713288f2ad92…
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