关于解决人工智能对健康公平方面的偏见的考虑
Michael D Abràmoff1, Michelle E Tarver2, Nilsa Loyo-Berrios2
1Departments of Ophthalmology and Visual Sciences, and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA. michael-abramoff@uiowa.edu.
人工智能/机器学习 (AI/ML) 可以改善健康公平,但如果不解决偏见,可能会加剧差异. 这项工作提出了一个框架,用于识别和减轻AI/ML偏差在其整个生命周期中,以获得公平的医疗保健结果.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 生物医学信息学是生物医学信息学.
- 卫生公平性健康公平性
背景情况:
- 医疗公平是各种各样的医疗保健利益相关者的关键目标.
- 数字健康技术,特别是AI/ML,有潜力改善平等获得诊断和治疗的机会.
- 然而,如果不仔细管理偏见,AI/ML也可能加剧现有的健康差异.
研究的目的:
- 为医疗保健AI/ML提出一个扩展的产品总生命周期 (TPLC) 框架.
- 在所有生命周期阶段描述AI/ML系统中不良偏差的来源和影响.
- 教导利益相关者识别和减轻AI/ML偏见,以确保健康公平.
主要方法:
- 该研究提出了一个扩展TPLC框架,用于医疗保健AI/ML.
- 它概述了使用适当指标分析不良偏差的方法.
- 它讨论了减轻已确定的偏见的潜在策略.
主要成果:
- 拟议的框架详细介绍了每个AI/ML TPLC阶段的偏见来源和影响.
- 它强调了对分析偏见的指标的需求.
- 它建议减缓策略来解决不平等问题.
结论:
- 解决AI/ML偏见对于实现健康公平至关重要.
- 扩展TPLC框架为利益相关者提供了管理AI/ML偏差的路线图.
- 积极识别和减轻偏见将导致所有人群的更好的健康结果.
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