在基于AI的预测模型中导航公平性:理论构造和实际应用
S L van der Meijden1,2, Y Wang3, M S Arbous1
1Department of Intensive Care Medicine, The Leiden University Medical Center, Leiden, The Netherlands.
medRxiv : the preprint server for health sciences
|April 8, 2025
概括
确保人工智能 (AI) 医疗保健模型的公平性对于公平的结果至关重要. 这项研究确定了关键的公平度指标,如临床效用和统计均等性,用于在医学中实际实施人工智能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能伦理学 人工智能伦理学
- 医疗人工智能 医疗人工智能
背景情况:
- 人工智能 (AI) 预测模型越来越多地用于医疗保健.
- 确保人工智能公平对抗健康差异和实现公平的患者结果至关重要.
- 对公平的相互矛盾的定义对实际AI实施构成挑战.
研究的目的:
- 构建人工智能公平性从理论到实践的过渡.
- 为医疗人工智能应用确定适当的公平性指标.
- 评估公平性定义,预期使用,决策类型和分配正义之间的关系.
主要方法:
- 从最近的文献中审查了27个AI公平性的定义.
- 评估每个定义与预期使用,决策影响和道德原则的关系.
- 评估的临床效用,性能指标 (AUC),校准和医疗应用的统计平价.
- 通过两个用例证明了适用性.
主要成果:
- 临床实用性,基于绩效的指标 (AUC),校准和统计均等性被推为医疗AI最相关的基于群体的公平性指标.
- 根据具体的预期用途和道德框架,可以应用不同的公平度指标.
- 该研究为评估AI公平性和偏见缓解提供了基础.
结论:
- 为了实现公平的医疗保健实施,需要对AI公平性指标采取结构化的方法.
- 选择适当的公平度指标取决于临床实用性和伦理考虑的上下文.
- 这项工作促进了更公平的AI开发和部署在医疗保健.
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