医学AI中的偏见:对临床决策的影响
James L Cross1, Michael A Choma2, John A Onofrey2,3,4
1Yale School of Medicine, New Haven, Connecticut, United States of America.
PLOS digital health
|November 7, 2024
概括
医学人工智能 (AI) 的偏见可能导致临床决策不佳,并加剧医疗保健差异. 在整个AI生命周期中解决这些偏见对于公平的患者结果至关重要.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗保健的不平等 医疗保健的不平等
- 临床决策 - 临床决策
背景情况:
- 医学AI中的偏见可以在整个AI生命周期中出现和复合.
- 未解决的偏见可能导致不符合标准的临床决策,并加剧医疗保健差异.
- 这些问题在涉及临床决策的AI应用中尤为关键.
研究的目的:
- 讨论医学人工智能开发中的潜在偏见.
- 探索这些偏见如何影响人工智能算法和临床决策.
- 概述解决方案,以减轻医学AI中的偏见.
主要方法:
- 在人工智能开发管道 (数据,模型开发,部署,出版) 中审查潜在的偏见来源.
- 分析不足的数据,缺少的发现,有偏见的标签和绩效指标如何导致偏见.
- 讨论最终用户互动和出版实践作为偏见的来源.
主要成果:
- 偏差可以表现为数据特征,标签,模型评估,部署交互和出版环境.
- 样本规模不足和患者数据缺失 (例如健康的社会决定因素) 会导致表现不佳和预测偏差.
- 过度依赖绩效指标和偏见的培训标签可以掩盖偏见并降低临床效用.
结论:
- 缓解策略包括收集各种数据,使用统计数据,严格评估,并强调可解释性.
- 标准化的偏见报告和透明度至关重要.
- 临床试验对于在实际实施之前验证无偏见的应用至关重要,以确保公平的患者利益.
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