对临床AI公平性进行范围审查和证据差距分析
Mingxuan Liu1, Yilin Ning1, Salinelat Teixayavong1
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
NPJ digital medicine
|June 14, 2025
概括
人工智能 (AI) 在医疗保健中的公平性需要解决偏见并促进公平. 本次审查确定了人工智能公平性研究的差距,并提出了公平医疗保健提供战略.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 人工智能伦理学 人工智能伦理学
- 卫生公平性健康公平性
背景情况:
- 人工智能 (AI) 在医疗保健中的伦理整合至关重要.
- 人工智能公平性涉及减轻偏见和促进医疗保健应用中的公平性.
- 人工智能技术解决方案与临床实践之间存在分歧.
研究的目的:
- 系统地识别医疗保健环境和人工智能公平技术的交叉点上的差距.
- 分析人工智能公平性中的证据差距,用于医疗保健中的偏见检测,评估和缓解.
- 提出推进医疗保健中人工智能公平性的战略.
主要方法:
- 进行了证据差距分析.
- 审查的重点是医疗保健环境 (医疗领域,数据集,与偏见相关的属性) 和AI公平性技术.
- 进行了研究缺口的系统识别.
主要成果:
- 在特定的医疗领域,AI公平性研究的稀缺性.
- 仅限于关注各种与偏见相关的属性 (例如性别/性别).
- 群体公平性指标的主导地位和临床医生在AI公平性方面的参与有限.
结论:
- 提出了可行的策略,以弥补人工智能公平性研究中发现的差距.
- 加快人工智能公平性发展对于公平的医疗保健提供至关重要.
- 未来的研究应该解决已识别的局限性,以改善AI在临床环境中的公平性.
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