人工智能偏差的影响:对心血管成像的考虑
Marly van Assen1, Ashley Beecy2,3, Gabrielle Gershon4
1Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA. marly.van.assen@emory.edu.
Current atherosclerosis reports
|February 16, 2024
概括
人工智能 (AI) 对心血管疾病 (CVD) 的偏见可能会伤害患者. 本指南有助于AI开发人员理解数据和指标假设,以防止有偏见的AI模型,并提高现实世界的心血管成像性能.
科学领域:
- 医学中的人工智能
- 心血管成像 - 心血管成像
- 卫生公平性健康公平性
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,其结果在人口统计学上有很大差异.
- 用于人工智能培训的现有健康数据往往反映了临床试验,诊断和治疗的历史不平等.
- 人工智能模型可以延续甚至放大这些偏见,对患者护理产生负面影响.
研究的目的:
- 为人工智能开发团队提供全面的指南,用于识别和减轻心血管AI中的偏见.
- 突出数据集假设和指标选择对心血管疾病中的真实世界AI性能的影响.
- 审查不同人群中心血管疾病结果和治疗的差异.
主要方法:
- 对心血管疾病数据集中的隐式和显式偏差的审查.
- 对心血管疾病结果,治疗和临床试验代表性的差异分析.
- 检查现有的AI文献,以寻找心血管AI中偏差和偏差缓解的例子.
主要成果:
- 心血管AI模型可能会由于训练数据的内在差异而表现出偏见.
- 偏见可以表现为性别和种族群体之间的差异性表现.
- 人工智能还为心血管应用中的偏见审计和缓解提供了潜在的工具.
结论:
- 了解数据集假设和指标选择对于开发心血管成像中公正的AI至关重要.
- 解决人工智能的偏见对于确保心血管疾病护理中公平的患者结果至关重要.
- 人工智能开发团队必须主动审计和减轻偏见,以防止临床实践中的负面影响.
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