用大模型把临床案例变成长期互动游戏，提升医学学习沉浸感。
MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
- 将临床病例转为可交互的叙事游戏，含状态与决策节点。
- 5000例数据集验证，微调后开源模型性能接近商用模型。
- 学生测试显示比纯文本更有趣、更易学，适合医学教学。
大型语言模型在医学教育中展现潜力，但现有系统多局限于问答或单轮反馈，缺乏对完整临床案例的决策导向式学习路径构建。本文提出MedGame框架，将静态临床案例转化为结构化、可执行的叙事游戏。该框架采用双引擎设计：医学叙事设计师生成基于病例的临床故事线及状态与决策节点；故事导演则将其转化为依赖感知的多模态编排计划，并由我们发布的交互平台实现渲染。我们构建了包含5000个案例的MedGame Bench基准数据集与评估协议，用于医学叙事生成与故事导演任务。实验表明，针对特定任务的微调能显著提升开源LLM在该基准上的表现，缩小与商用模型的差距。初步学生研究显示，学习者认为MedGame比纯文本更吸引人且更具实用性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical storylines with states and decision nodes, while a Story Director converts them into dependency-aware multimodal orchestration plans rendered by our released interactive platform. We construct MedGame Bench, a 5,000-case benchmark and evaluation protocol for Medical Narrative Generation and Story Direction. Experiments show that task-specific fine-tuning substantially improves open-source LLMs on MedGame Bench and narrows the gap with commercial models. A pilot student study further shows that learners perceive MedGame as more engaging and useful than text-only alternatives.
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