为护士科学家和协调健康研究人员建立人工智能和偏见缓解能力
Michael P Cary1, Siobahn D Grady2, Jacquelyn McMillian-Bohler3
1School of Nursing, Duke University, Durham, NC; Duke AI Health, Duke University School of Medicine, Durham, NC.
Nursing outlook
|May 3, 2025
概括
新的培训计划解决了医疗保健中使用的人工智能 (AI) 和机器学习 (ML) 工具的偏见. 该倡议旨在通过为科学家提供识别和减轻算法不平等的技能来促进健康公平.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 健康 公平 研究 健康 公平 研究
背景情况:
- 医疗保健系统正在迅速采用人工智能和机器学习 (AI/ML) 来为患者护理.
- 人们越来越担心AI/ML工具会延续系统性不平等和健康差异.
- 需要制定缓解偏见的指导方针和员工培训,以确保公平的AI/ML实施.
研究的目的:
- 开发一个全面的培训计划,解决医疗保健AI/ML算法中嵌入的结构性不平等问题.
- 为科学家提供必要的能力,以识别,分析和减轻算法偏差.
- 培养一个包容性的生态系统,以促进医疗保健中的道德人工智能治理.
主要方法:
- 进行了全面的范围审查,以告知课程开发.
- 设计和实施多方面的培训研讨会和研究研讨会.
- 来自临床医生,生物统计学家,工程师和政策制定者的综合观点.
主要成果:
- 开发了以人类为中心的多学科人工智能用于下一代教育和研究 (HUMAINE) 的课程.
- 该课程侧重于算法中的结构不平等,导致健康差异.
- 培训科学家解决算法偏见并促进健康公平.
结论:
- HUMAINE课程为解决健康不平等问题提供了必要的AI/ML能力.
- 多学科合作是利用人工智能为公平医疗保健提供潜力的关键.
- 伦理的人工智能治理对于创造一个更加包容和公平的医疗保健未来至关重要.
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