对临床AI公平性的翻译视角
Mingxuan Liu1, Yilin Ning1, Salinelat Teixayavong1
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
NPJ digital medicine
|September 14, 2023
概括
人工智能 (AI) 在临床环境中的公平性需要以公平为重点的方法,而不仅仅是跨组的平等表现. 合作是确保人工智能对医疗保健负责任的关键.
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
- 临床决策支持 临床决策支持
背景情况:
- 人工智能证明了数据洞察力提取,但在医疗保健等高风险领域引发了公平性问题.
- 现有的AI公平度指标和缓解方法可能与临床环境不一致.
- 临床应用需要仔细检查敏感变量和公平度指标的伦理含义.
研究的目的:
- 在临床环境中解决当前AI公平性方法的不足.
- 为临床人工智能公平性提出一个基于公平性的框架.
- 强调需要多学科合作,在医疗保健中实现公平的人工智能.
主要方法:
- 在医疗保健的背景下,对标准AI公平性定义和测量进行批判性分析.
- 检查公平度指标中的伦理考虑和潜在冲突.
- 强调临床理由对子组绩效差异的作用.
主要成果:
- 标准的AI公平性定义 (平等) 往往与临床现实不一致.
- 临床人工智能公平性应该优先考虑公平,而不是严格的平等.
- 临床反和临床医生的参与对于开发公平的人工智能模型至关重要.
结论:
- 将人工智能公平性适应于医疗保健需要从"平等"转向"公平".
- 人工智能研究人员,临床医生和伦理学家之间的多学科合作至关重要.
- 弥合技术AI公平性和临床考虑之间的差距对于现实世界的好处至关重要.
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