重新思考人工智能的公平性,以改善瘤学当前的实践
Salamata Konate1, Jack Gallifant2, Charles Senteio3
1York University, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.
Trends in cancer
|December 3, 2025
概括
评估人工智能 (AI) 在瘤学中的公平性是具有挑战性的,因为有偏见的数据和指标. 需要新的公平框架,以确保为不同患者群体提供公平的癌症护理.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 健康 公平 卫生 公平
背景情况:
- 人工智能 (AI) 的公平性至关重要,特别是在瘤学中.
- 现有的公平度量通常是有缺陷的,并且不考虑现实世界的不平等.
- 患者多样性和结构偏见对癌症护理结果产生重大影响.
研究的目的:
- 突出了当前对瘤学AI公平性指标的局限性.
- 强调需要修订公平性框架,以解决数据和预测中的偏见.
- 通过更好的AI评估,倡导提高癌症护理的公平性.
主要方法:
- 分析地面真相标签,人工智能预测和人口统计学属性的偏见.
- 在瘤学背景下批评标准公平性指标.
- 在癌症护理中为公平的人工智能开发概念框架.
主要成果:
- 目前用于瘤学的人工智能的公平性指标是不充分的.
- 在数据集,预测和人口统计数据中存在偏见,扭曲了公平性评估.
- 目前的人工智能公平性评估与癌症护理中的真正公平性之间存在很大的差距.
结论:
- 重新思考人工智能公平性框架对于瘤学来说至关重要.
- 解决数据和指标中的偏差对于实现健康公平至关重要.
- 需要新的方法来确保人工智能促进公平的癌症治疗结果.
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