考虑对临床和教育数据的二次使用,以促进人工智能模型的开发
概括
教育和临床数据越来越多地用于培训医疗教育中的人工智能 (AI) 模型. 这种二次数据使用在有效性,同意和偏见方面带来了挑战,需要对AI产品开发进行仔细考虑.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 医疗培训和医疗保健系统产生大量的教育和临床数据.
- 对于在项目评估和质量改进中使用二次数据,已有确定的指导方针.
- 为了训练人工智能 (AI) 模型,这些数据的新兴使用需要探索.
研究的目的:
- 探索使用教育和临床数据用于AI模型开发的含义.
- 讨论AI产品在医学教育中的应用.
- 概述在AI中适当使用学习者数据的考虑因素.
主要方法:
- 在医学教育和人工智能中使用二级数据的文献综述.
- 对挑战的分析,包括AI输出中的有效性,同意和偏见.
- 案例研究检查一个教育协作导航这些问题.
主要成果:
- 在医疗数据上训练的人工智能模型可以导致具有潜在教育益处的可商业化产品.
- 有关数据有效性,学习者/患者同意,以及潜在的有偏见的AI输出存在重大挑战.
- 教育协作为解决这些数据使用考虑提供了一个框架.
结论:
- 医疗数据的二次使用对人工智能开发需要对道德和实际影响进行彻底审查.
- 需要明确的指导方针,以确保负责任的AI开发和部署在医学教育.
- 在医疗保健教育中平衡创新与患者和学习者的权利对于可信的人工智能至关重要.
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