一个基于证据的方法,人工智能教育为医学学生:一个系统的审查
Nikola Pupic1, Aryan Ghaffari-Zadeh1, Ricky Hu2
1Faculty of Medicine, University of British Columbia, British Columbia, Vancouver, Canada.
PLOS digital health
|November 27, 2023
概括
医学院需要整合人工智能 (AI) 教育. 这一系统性审查确定了人工智能课程的六个关键主题,包括道德和沟通,以准备未来的医生.
科学领域:
- 医疗教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 为提高患者护理质量提供了巨大的潜力.
- 医学教育在适应人工智能方面落后,导致医学院的AI专用培训不足.
研究的目的:
- 系统地审查基于证据的建议,将人工智能教育课程纳入本科医学课程.
主要方法:
- 在2022年4月23日之前,对六个数据库进行了系统搜索.
- 包括的研究包括横截面,队列,系统性审查,范围审查,整合性审查,随机对照试验和Delphi研究.
- 对34项选定的研究进行了主题分析.
主要成果:
- 在医学课程中确定了人工智能的六个核心主题:伦理,理论和应用,沟通,协作,质量改进以及感知和态度.
- 副主题包括以患者为中心/以数据为中心的伦理,实践/沟通知识以及各种沟通方面.
- 医疗专业人员和学生,尽管基线AI知识较低,但强烈支持正规AI教育.
结论:
- 结构化的AI课程对于本科医学教育至关重要.
- 需要进一步的研究来评估人工智能教育策略的影响,并推进课程开发.
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