在初级医疗保健中减轻偏见的人工智能模型:范围审查
Maxime Sasseville1,2, Steven Ouellet1, Caroline Rhéaume2,3,4
1Faculté des sciences infirmières, Université Laval, Québec, QC, Canada.
Journal of medical Internet research
|January 8, 2025
概括
人工智能 (AI) 医疗保健模型中的偏差可以通过数据预处理和开源来减少. 让各种各样的利益相关者参与并使用"人与人"的方法是初级保健中公平人工智能的关键.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 算法偏见是一种算法偏见.
背景情况:
- 在初级医疗保健中,人工智能 (AI) 预测模型为改善人口健康提供了潜力.
- 然而,这些模型有可能延续或放大对不同群体的偏见.
- 在了解初级医疗保健算法中评估和减轻偏差的策略方面存在差距.
研究的目的:
- 在初级医疗保健人工智能模型中描述偏见缓解策略.
- 识别目标的多元化群体和受保护的属性.
- 评估这些策略对减少偏差和模型性能的影响.
主要方法:
- 根据乔安娜·布里格斯研究所 (JBI) 的指导方针进行了范围审查.
- 这些搜索包括Medline,CINAHL,PsycINFO和Web of Science数据库 (2017年1月至2022年11月).
- 在包括的17项研究中进行了数据提取和质量评估.
主要成果:
- 种族/种族和性别是最常被调查的受保护属性.
- 偏见缓解方法包括数据/模型修改,电子健康记录采购,人身循环系统和道德原则.
- 算法预处理 (重新标记,重新权衡) 和自然语言处理显示出有希望的结果,但有时会加剧错误.
结论:
- 偏差缓解在开源数据,利益相关者的参与以及在预处理阶段更有效.
- 需要进一步的经验研究,包括更广泛的群体范围.
- 对发现的验证和扩展对于初级保健中公平的AI至关重要.
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