验证人工智能决策支持的种族和民族非偏见,用于诊断乳腺超声波评估
Clara Koo1, Anthony Yang1, Colton Welch1
1Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, Diagnostic Molecular and Interventional Radiology, New York, New York, United States.
Journal of medical imaging (Bellingham, Wash.)
|December 18, 2023
概括
这项研究发现,用于乳房超声波的人工智能 (AI) 决策支持系统在其性能中没有显著的种族或种族偏见. 人工智能工具可以帮助放射科医生检测乳腺癌,而不会加剧医疗保健差距.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 健康差异 在健康上的差异
背景情况:
- 乳房超声波在积极预测价值和特异性方面存在局限性.
- 人工智能 (AI) 具有提高乳腺成像准确度和减少乳腺成像变异性的潜力.
- 确保人工智能工具不会延续现有的种族/民族医疗保健差异至关重要.
研究的目的:
- 评估AI决策支持 (DS) 系统的种族和种族偏见.
- 在乳房超声波中评估AI DS系统在不同患者群体中的性能.
- 确保人工智能在乳腺癌诊断中的公平应用.
主要方法:
- 1810个经过活检证明的乳房超声波病例的回顾性审查.
- 分析患者的人口统计 (年龄,性别,种族/种族) 和AI DS输出.
- 跨种族/种族群体的AI DS系统的分层性能分析.
主要成果:
- 在种族/种族群体之间观察到乳腺病理发病率的差异.
- AI DS系统输出差异归因于潜在的病理变异,而不是偏差.
- 不同种族/种族群体在AI DS系统输出中没有发现统计学意义上的偏差.
结论:
- 人工智能DS系统是一个有前途的工具,可以通过超声波帮助检测乳腺癌.
- 在这个队列中,人工智能系统在种族和民族群体中表现出类似的有效性.
- 诊断中的AI应该支持,而不是取代放射科医生,确保公平的患者结果.
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