人工智能的算法公平性用于医学和医疗保健
Richard J Chen1,2,3,4, Judy J Wang1,5, Drew F K Williamson1,3
1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Nature biomedical engineering
|June 28, 2023
概括
医疗保健中的不公平的人工智能 (AI) 会导致患者诊断,治疗和计费方面的差异. 新兴技术旨在减轻这些算法偏差,以便提供更公平的护理.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 机器学习的公平性
- 临床人工智能 临床人工智能
背景情况:
- 在医疗保健中部署不公平的人工智能 (AI) 系统损害了公平的医疗服务.
- 对人工智能模型的分层评估显示,在患者诊断,治疗和计费方面,在各个子群体之间存在显著的不平等.
- 人工智能中的算法偏见导致医疗保健差异.
研究的目的:
- 在医疗保健背景下概述机器学习中的公平性.
- 讨论在临床工作流程中算法偏差的起源.
- 审查新兴技术,以减轻医疗设备中的AI偏见.
主要方法:
- 在医疗保健中应用的机器学习原则中的公平性审查.
- 分析数据采集,遗传变异和观察者内部标签中的偏见来源.
- 探索偏差缓解技术,如解,联合学习和模型可解释性.
主要成果:
- 从数据采集,遗传变异和标签变化中产生的算法偏差会导致医疗保健差异.
- 新兴技术为缓解人工智能系统偏差提供了潜在的解决方案.
- 公平考虑对于开发公平的基于人工智能的医疗设备至关重要.
结论:
- 解决算法偏见对于确保在医疗保健中公平部署AI至关重要.
- 在解,联合学习和可解释性方面的技术进步是缓解偏见的关键.
- 开发公平的AI软件作为医疗设备对于减少医疗保健差异至关重要.
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