医疗人工智能融合教育的制度化
Tae In Park1, Jongmo Seo2, Hyung-Jin Yoon3
1Department of Medicine, Seoul National University College of Medicine, Seoul, 03080 Republic of Korea.
Biomedical engineering letters
|November 24, 2025
概括
医学院需要综合人工智能 (AI) 培训. 首尔国立大学医学院为人工智能融合教育开发了一个可扩展的模块化框架,为全球机构提供可转移的见解.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 人工智能 (AI) 越来越多地成为医疗保健的组成部分.
- 医疗教育需要转向全面的融合培训.
- 现有的方法通常依赖于试点计划或分散的课程变化.
研究的目的:
- 在SNU医学院展示了将人工智能整合到医学教育中的5年实例研究.
- 为医疗AI提供可扩展和可持续的融合教育提出框架.
- 为全球医疗机构提供可转移的见解.
主要方法:
- 在首尔国立大学医学院进行了为期5年的病例研究的叙事回顾.
- 开发一个以治理为导向的模块化框架.
- 整合机构基础设施,跨学科教学和政策调整.
主要成果:
- SNU医学建立了一个多层次的模型,用于将AI整合到医学教育中.
- 该模型具有一个模块化框架,包括基础设施,教学和政策.
- 确定了四个关键的设计原则:模块化,跨学科的协调,基础设施和课程的结合,以及政策的嵌入.
结论:
- 一种系统的,多层次的方法对于有效地将人工智能整合到医学教育中至关重要.
- 拟议的框架为融合培训提供了一个可扩展和可持续的模型.
- 该模型为全球医疗机构提供了宝贵的见解,特别是在受政策限制的环境中.
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