用智能体框架提升医学生模拟诊疗训练的逼真度与教学价值
An Agentic AI Framework for Training General Practitioner Student Skills
- 构建可配置的医学情景生成、人格化对话与标准化评估一体框架
- 14名医学生测试显示对话真实、反馈有用且难度适中
- 适合医学教育者开发高仿真、可扩展的虚拟患者训练系统
大型语言模型的发展为医学教育中的虚拟模拟患者（VSP）提供了强大潜力，可替代资源密集的传统方法。然而现有VSP常面临医学准确性不足、角色扮演不一致、情景生成困难及缺乏结构化反馈等问题。本文提出一种智能体框架，整合（i）可配置的循证情景生成，（ii）受控的人格驱动对话（支持检索增强），以及（iii）基于标准的沟通与临床推理评估与反馈。我们在交互式语音问诊场景中实现该框架，并对14名医学生进行评估。参与者报告对话真实、符合情景设定，人格特征稳定，难度适中，反馈内容丰富且极具实用性，整体可用性优异。结果表明，将情景控制、交互控制与标准评估分离开来的智能体架构，是构建可靠且具教学价值的VSP工具的可行范式。
原文摘要 · Abstract (English)
Advancements in large language models offer strong potential for enhancing virtual simulated patients (VSPs) in medical education by providing scalable alternatives to resource-intensive traditional methods. However, current VSPs often struggle with medical accuracy, consistent roleplaying, scenario generation for VSP use, and educationally structured feedback. We introduce an agentic framework for training general practitioner student skills that unifies (i) configurable, evidence-based vignette generation, (ii) controlled persona-driven patient dialogue with optional retrieval grounding, and (iii) standards-based assessment and feedback for both communication and clinical reasoning. We instantiate the framework in an interactive spoken consultation setting and evaluate it with medical students ($\mathbf{N{=}14}$). Participants reported realistic and vignette-faithful dialogue, appropriate difficulty calibration, a stable personality signal, and highly useful example-rich feedback, alongside excellent overall usability. These results support agentic separation of scenario control, interaction control, and standards-based assessment as a practical pattern for building dependable and pedagogically valuable VSP training tools.
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