推促进生物医学AI研究和临床使用中的公平性和包容性
Ashley C Griffin1, Karen H Wang2, Tiffany I Leung3
1Veterans Affairs Palo Alto Health Care System, Palo Alto, California and Stanford University School of Medicine, Stanford, California, USA.
Journal of biomedical informatics
|July 17, 2024
概括
解决医疗保健人工智能 (AI) 的偏见对于公平性至关重要. 这项研究概述了偏见如何进入人工智能开发,并为公平的生物医学应用提供了建议.
科学领域:
- 生物医学科学 生物医学科学
- 医疗信息学 医疗信息学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 人工智能 (AI) 越来越多地用于生物医学应用,需要关注公平和包容性.
- 了解和量化人工智能的偏见对于开发公平的医疗保健系统至关重要.
- 在AI开发和部署的各个阶段可以引入偏见.
研究的目的:
- 讨论在生物医学科学和医疗保健领域的AI应用中引入偏见的问题.
- 探索人工智能在不同发展阶段对公平性和包容性的影响.
- 为减轻临床AI偏见提供可行的建议.
主要方法:
- 讨论数据源景观中的偏差.
- 对算法公平原则的检查.
- 在AI预测中的不确定性分析.
- 探索可解释的人工智能,以实现公平和公平.
- 在数据和结果表示中考虑社会学和民族学问题.
主要成果:
- 在医疗保健的AI生命周期中识别偏见引入点.
- 对人工智能对公平和包容性的影响进行分析.
- 制定建议,以减轻临床AI中的偏见.
结论:
- 提供了建议,以解决AI开发和用于临床应用的偏见.
- 这些策略可以提高公平性和包容性在健康信息学研究和实践.
- 其目标是促进更公平的医疗保健系统和研究发现.
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