在人工智能时代,重新想象本科医学教育的核心可信任的专业活动
Sarah Marie Jacobs1, Neva Nicole Lundy1, Saul Barry Issenberg1
1Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, United States.
生成型人工智能 (AI) 需要对医学教育的可信任专业活动 (EPA) 进行更新. 这项研究评估了当前的EPA,并提出了新的EPA,以为训练员准备人工智能集成的医疗保健未来.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 健康 职业 教育 教育 专业
背景情况:
- 生成型人工智能 (AI) 正在迅速融入医疗保健,需要改变医疗培训.
- 美国医学院协会 (AAMC) 可信任的专业活动 (EPAs) 框架指导本科医学教育.
- 现有的AAMC EPA可能无法充分为AI驱动的医疗环境做好培训人员的准备.
研究的目的:
- 在医学中生成人工智能集成的背景下,批判性地评估当前的AAMC核心EPA.
- 确定需要改进,重新定义或全面改变的EPA.
- 通过人工智能的进步提出新的"新兴"EPA.
主要方法:
- 对现有的AAMC核心环保协议进行批判性评估.
- 分析生成性AI对医疗保健和医学教育的影响.
- 使用EPA框架制定适应医学教育的建议.
主要成果:
- 特定的AAMC核心EPA需要修改,以解决AI在医疗保健中的作用.
- 生成型人工智能需要创建新的,前性的EPA.
- 提供了可行的建议,以使医学教育与AI集成保持一致.
结论:
- 医疗教育必须调整其可信任的专业活动 (EPAs),以整合生成AI.
- 完善和创建新的EPA将确保学员拥有必要的技术能力.
- 这种主动的方法为未来的医疗保健专业人员准备了人工智能集成的医疗未来.
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