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  3. A Large Language Model-powered Multiagent Framework Emulating Standardized Patients In Clinical Communication Skills Training: Development And Evaluation Study.
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  3. A Large Language Model-powered Multiagent Framework Emulating Standardized Patients In Clinical Communication Skills Training: Development And Evaluation Study.

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A Large Language Model-Powered Multiagent Framework Emulating Standardized Patients in Clinical Communication Skills

Yufei Qu1, Xiaowei Xu1,2, Yunzi Long3,4

  • 1College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310058, China.

Journal of Medical Internet Research
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
communication skillslarge language modelsmedical educationmultiagentvirtual patient

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A new multiagent virtual patient framework effectively simulates standardized patients for medical training, outperforming single large language models in role-playing and interaction fidelity. This approach enhances clinical communication skills for students.

Area of Science:

  • Artificial Intelligence in Medical Education
  • Computational Linguistics for Healthcare Simulation
  • Multiagent Systems in Clinical Training

Background:

  • Effective clinical communication is vital in medical practice.
  • Standardized patients (SPs) are a reliable training method but resource-intensive.
  • Current virtual patients (VPs) using single large language models (LLMs) face fidelity and interaction limitations.

Purpose of the Study:

  • To develop and evaluate a novel multiagent VP framework simulating SPs.
  • To enhance human-like fidelity and interaction performance in clinical communication training.
  • To leverage collaborative agent design for improved VP simulation.

Main Methods:

  • Constructed a multiagent framework with 5 specialized subagents simulating brain region functions.
  • Incorporated retrieval-augmented technology and deep character reasoning for enhanced interaction.
  • Evaluated the framework by comparing base models and benchmarking against single-LLM baselines using metrics like response quality and role-playing performance.

    • Main Results:

    • The multiagent framework surpassed single-LLM baselines in accuracy and role-playing under standardized conditions.
    • GPT-4o implementation achieved 0.769 factual consistency; all configurations maintained >94% clinical accuracy.
    • Qwen3-32B framework showed a low misleading rate (1.28%) and high role-playing competency (39.67), with practical interaction latency (~3s).

    Conclusions:

    • The multiagent framework provides a viable, customizable, and scalable simulation of SPs for medical communication training.
    • This approach enhances VP performance, maintaining patient confidentiality.
    • The framework shows significant potential for advancing medical education methodologies.