乳腺癌风险预测中的不确定性:对种族分层的合规预测研究
Alexander S Millar1, John Arnn1, Sam Himes1
1Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108, USA.
Studies in health technology and informatics
|January 25, 2024
概括
医学中的人工智能 (AI) 正在进步,但评估预测不确定性是关键. 符合性预测 (CP) 在乳腺癌风险评估中显示少数种族群体的预测质量较低.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 人工智能 (AI) 越来越多地用于医疗应用.
- 在临床环境中评估AI预测的不确定性仍然是一个挑战.
- 了解不同人口群体的预测不确定性对于公平的医疗保健至关重要.
研究的目的:
- 展示符合预测 (CP) 在AI驱动的医学预测中的不确定性量化应用.
- 调查乳腺癌风险预测不确定性的种族差异.
- 突出评估AI模型在不同人群中的表现的重要性.
主要方法:
- 使用符合预测 (CP) 技术来量化预测不确定性.
- 将CP应用于乳腺癌风险预测模型.
- 分析了基于种族人口统计学的不确定性分层.
主要成果:
- 符合预测 (CP) 成功量化了乳腺癌风险的预测不确定性.
- 在种族群体之间观察到预测质量的显著差异.
- 对于来自少数民族种族背景的个人,CP方法发现预测质量下降.
结论:
- 符合预测 (CP) 是评估AI预测不确定性在医学中的一个有价值的工具.
- 不确定性的种族分层对于识别和减轻人工智能医疗保健工具中的偏见至关重要.
- 这些发现强调了需要在临床实践中公平地开发和部署人工智能.
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