能否使用生成型人工智能模型创建大规模伤亡事件模拟场景? 一个可行性研究
Sergio M Navarro1, Angie G Atkinson2, Ege Donagay3
1Division of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
Healthcare (Basel, Switzerland)
|December 30, 2025
概括
生成型人工智能 (AI) 可以创建大规模伤亡事件 (MCI) 模拟场景. 将人工智能生成与专家人类审查相结合,提高了培训的场景质量,尽管人类监督至关重要.
科学领域:
- 医疗模拟和培训 医学模拟和培训
- 医疗保健中的人工智能
- 创伤护理教育教育 创伤护理教育
背景情况:
- 大规模伤亡事件 (MCI) 模拟场景对于多学科创伤小组的准备工作至关重要.
- 目前的场景开发是资源密集型的,依赖于专家团队的详细规划.
- 评估生成AI的可行性,以创建这些复杂的培训工具是必不可少的.
研究的目的:
- 评估使用生成人工智能 (AI) 开发大规模伤亡创伤模拟场景的可行性.
- 与传统方法相比,评估人工智能生成的场景的质量和可靠性.
- 探索一种新的工作流程,将人工智能与人类专业知识相结合,用于模拟开发.
主要方法:
- 使用基于美国公共创伤数据的大型语言模型 (LLM) 平台 (ChatGPT4) 创建了十个复杂的MCI创伤模拟场景.
- 两个高级创伤生命支持 (ATLS) 认证评级人员使用经过验证的模拟场景评估工具 (SSET) 评估了场景.
- 将LLM场景与两个控制场景进行了比较;进行了反复的人类修订和重新评估.
主要成果:
- 最初的LLM产生的场景在SSET中获得了78.5的中位数,远低于对照场景 (中位数94).
- 在初始的LLM场景中观察到高的interrater可靠性 (ICC 0.965).
- 经过修订后,LLM场景实现了SSET中位数94的中位数,提高了评分器间可靠性 (ICC 0.7425).
结论:
- 结合LLM生成与专家人类审查的协作工作流显示出创建MCI模拟场景的前景.
- LLM为创伤培训材料的开发提供了可扩展的解决方案,提高了效率.
- 基本的人类监督,质量保证和治理对于确保临床准确性和教育价值至关重要.
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