Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education.

ABSTRACT:

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 patient system powered by LLM-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 (Knowled

SUBMITTER: Yu H 

PROVIDER: S-EPMC12808140 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature