在临床决策中促进公平:拆除基于种族的医学
Tina Hernandez-Boussard1, Shazia Mehmood Siddique2, Arlene S Bierman3
1Tina Hernandez-Boussard (boussard@stanford.edu), Stanford University, Stanford, California.
Health affairs (Project Hope)
|October 2, 2023
概括
医疗保健中的人工智能可以通过有缺陷的算法来延续种族偏见. 考虑社会决定因素的种族意识的方法对于减少健康差异和促进公平护理至关重要.
科学领域:
- 卫生公平性健康公平性
- 医学中的人工智能.
- 算法偏差是一种算法偏差.
背景情况:
- 医疗保健算法中的种族和种族偏见越来越令人担忧.
- 基于种族的医学,使用种族作为生物学的代理,导致少数群体的有害治疗差异.
研究的目的:
- 在临床决策中使用的人工智能算法中解决种族和种族偏见.
- 提出一种种族意识的方法来缓解健康差异.
主要方法:
- 对美国医疗保健当前人工智能应用的分析.
- 审查政策变化和算法开发创新.
- 关于种族意识的临床决策支持框架的建议.
主要成果:
- 现有的AI算法经常嵌入基于种族的医学,导致不适当和不公正的治疗模式.
- 通过政策变化和技术创新,正在取得进展.
结论:
- 纳入健康的社会决定因素的种族意识的方法对于拆除基于种族的医学至关重要.
- 需要持续的利益相关者承诺,以增加试验多样性,扩大精准医学,改善教育,并制定文化响应的护理政策.
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