迈克尔·波兰尼的见解:默契知识及其在医学教育中的关键重要性
Thomas J Papadimos1, Justin Hsu2, Scott M Pappada3,4
1Anesthesiology, Surgery, and Critical Care, The University of Toledo College of Medicine and Life Sciences, Toledo, USA.
Cureus
|February 25, 2026
概括
人工智能 (AI) 和机器学习 (ML) 正在改变医学教育,可能会影响临床教学. 这项研究探讨了AI/ML是否可以支持默认的知识转移,或者传统方法是否会被取代.
科学领域:
- 医学教育 医学教育
- 数字健康数字健康
- 人工智能的人工智能
背景情况:
- 第四次工业革命正在通过数字技术改变医学教育.
- 虚拟学习和AI/ML正在增加,对传统的床边教学构成风险.
- 医学中的默契知识转移传统上依赖于直接的指导和互动.
研究的目的:
- 调查AI和ML在医学教育中的作用.
- 确定AI/ML是否可以有效支持默认知识的转移.
- 探索AI/ML的潜在替代病床教学.
主要方法:
- 在医学教育中对AI/ML的文献综述.
- 分析隐性知识转移理论的分析.
- 虚拟与床边教学教学有效性的比较研究.
主要成果:
- 人工智能/机器学习为学习和知识传播提供了新的途径.
- 在复制床边教学与人工智能的微妙互动方面存在挑战.
- 整合AI/ML可能会增强,而不是完全取代传统方法.
结论:
- 人工智能和机器学习可以在医学教育中作为有价值的辅助工具,用于默认的知识转移.
- 需要进行进一步的研究,以优化AI/ML集成,同时保持床边教学的基本元素.
- 技术与传统指导相结合的混合方法可能是未来医疗培训的最佳选择.
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