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

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Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making.

• Advances in artificial intelligence (AI), especially large language models (LLMs), can enhance simulated patients by replicating medical conditions and doctor patient interactions with high fidelity and at low cost, but effectiveness and trustworthiness remain open challenges.
• Methods: We developed AIPatient, a simulated patient system powered by LLM based AI agents.
• The system uses a retrieval augmented generation (RAG) framework with six task specific agents for complex reasoning.