模拟报告中的生成人工智能:使用Team-FIRST框架的探索性研究以及模拟专家和学习者的定性反.
David W Tscholl1, Max Ebensperger2, Arend RahrischRahrisch1
1Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Advances in simulation (London, England)
|January 30, 2026
概括
生成型人工智能 (AI) 可以通过分析团队沟通和提供反来帮助医疗模拟报告. 然而,由于人工智能,人类监督至关重要.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 基于模拟的培训
背景情况:
- 基于模拟的教育需要有效的汇报,以获得最佳的学习.
- 辅导员在观察团队互动方面面临挑战,原因是认知负载和偏见.
- 生成型人工智能提供了分析通信和支持医学模拟中的解答的潜力.
研究的目的:
- 探索生成人工智能工具在医学模拟期间观察团队合作的实用性.
- 评估人工智能生成的报告对模拟汇报流程的影响.
- 了解参与者和辅导员在模拟中使用人工智能的经验.
主要方法:
- 定性探索性研究,涉及对人工智能生成的报告和采访进行主题分析.
- 在苏黎世大学医院进行沉浸式医学模拟的41名参与者.
- 使用基于Team-FIRST框架的大型语言模型 (Isaac,ChatGPT-4o) 的语言交互的AI辅助转录和分析.
主要成果:
- 人工智能报告提供了详细的成绩单和引文,有助于结构化的反和捕捉错过的观察.
- 限制包括分类的不准确性,演讲者的错误归因,以及缺乏非语言上下文.
- 学习者对AI的效率和客观性持乐观态度,但担心透明度和错误;人类监督被认为是必不可少的.
结论:
- 生成型人工智能可以通过结构化通信数据和识别团队合作模式来增强模拟汇报.
- 目前的人工智能局限性需要多式联络方法和专家协助.
- 在基于模拟的教育中,人工智能应该作为支持工具,而不是替代.
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