在研究生医学教育中的生成人工智能
Ravi Janumpally1, Suparna Nanua1, Andy Ngo1
1Clinical Informatics Fellowship Program, Baylor Scott & White Health, Round Rock, TX, United States.
Frontiers in medicine
|January 27, 2025
概括
生成型人工智能 (GenAI) 为研究生医学教育 (GME) 提供了显著的机会,例如减少电子健康记录 (EHR) 的工作量和增强临床模拟. 然而,对人工智能的不准确性和偏见等风险进行仔细考虑对于负责任的整合至关重要.
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
- 研究生医学教育 (GME)
背景情况:
- 生成型人工智能 (GenAI) 正在越来越多地影响各种领域,包括医疗保健和教育.
- 研究生医学教育 (GME) 正在探索新技术的整合,以加强培训和实践.
研究的目的:
- 探索与在GME中实施GenAI相关的潜在机会和风险.
- 提供关于GenAI对GME关键领域的影响的评论.
主要方法:
- 关于GenAI在医疗保健和教育方面的现有研究的文献综述.
- 分析和讨论与GME相关的确定的机会和风险.
主要成果:
- 关键的机会包括EHR工作量减少,临床模拟,个性化教育,研究支持和临床决策支持.
- 显著的风险包括AI的不准确性,过度依赖,学术完整性的挑战,潜在的偏见和隐私问题.
结论:
- 对于GME的未来来说,GenAI具有变革性的潜力.
- 成功的整合需要全面了解GenAI的好处和局限性,以减轻风险.
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