重新调查人工智能分类算法在COVID-19X射线和CT图像上的性能
Rui Cao1, Yanan Liu1, Xin Wen1
1School of Software, Taiyuan University of Technology, Taiyuan 030024, China.
iScience
|May 1, 2024
概括
人工智能 (AI) 诊断模型在特定患者群体,特别是年轻女性中显示出低诊断偏差. 在多样化的数据集上训练人工智能可以提高模型的概括性,减少诊断差异.
科学领域:
- 医疗人工智能 医疗人工智能
- 健康 公平 卫生 公平
- 诊断偏见的诊断偏见
背景情况:
- 有关人工智能 (AI) 算法存在担忧,可能会导致低诊断偏差,将具有特定属性的患者 (例如女性,年轻人) 错误分类为健康.
- 解决人工智能偏见对于开发可靠的人工智能诊断至关重要,特别是对于像COVID-19这样迅速传播的传染病至关重要.
研究的目的:
- 调查流行AI诊断模型中的不足诊断偏差.
- 评估训练数据异质性对AI模型概括和性能的影响.
- 开发一个分析AI偏差在医疗应用中的管道.
主要方法:
- 对不同患者人口统计学中的低诊断率的流行AI诊断模型的评估.
- 在异质数据集上训练时,分析AI模型性能变化.
- 开发和应用一个AI偏差分析管道.
主要成果:
- 普遍存在的AI诊断模型在特定患者群体中表现出低诊断率,在交叉组 (例如20-40岁的女性) 中观察到更高的比率.
- 在异质数据集上训练人工智能模型可能会导致模型的概括性差,导致测试集之间显著的性能差异 (精度差异超过40%).
结论:
- 人工智能诊断工具可以通过低诊断偏见来延续健康差异.
- 开发了一个AI偏见分析管道,以帮助研究人员识别和减轻影响医疗平等和伦理的偏见.
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