胸部放射学偏差风险 深度学习基础模型
Ben Glocker1, Charles Jones1, Mélanie Roschewitz1
1From the Department of Computing, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Radiology. Artificial intelligence
|December 11, 2023
概括
胸部放射学基础模型表现出显著的种族和性别相关偏见,导致患者子组检测疾病的性能差异. 这种偏见引发了人们对其在医学成像中临床使用的安全性的担忧.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 放射学和诊断成像 放射学和诊断成像
- 健康公平性和算法偏见
背景情况:
- 基础模型越来越多地用于医学成像,但它们的偏差潜力需要仔细评估.
- 胸部放射图解读对于诊断许多疾病至关重要.
- 确保人工智能诊断工具的公平和公平对于患者安全和有效的医疗保健至关重要.
研究的目的:
- 为了研究最近发表的胸部放射学基础模型,研究与生物性别和种族有关的偏见.
- 确定这些偏见是否导致不同患者子组的绩效差异.
- 评估基础模型中发现的偏差对临床安全的影响.
主要方法:
- 从CheXpert数据集中对127,118张胸部X射线图进行了回顾性分析.
- 应用维度减小和科尔莫戈罗夫-斯米尔诺夫测试来检测模型生成特征的分布转移,跨性别和种族.
- 综合性疾病检测性能分析,将特征偏差与分类差异联系起来.
主要成果:
- 基础模型在性别和种族的特征预测中显示了统计学上显著的差异 (12项比较中的10项).
- 在女性患者 (6.8%-7.8%) 中",没有发现"标签上的表现下降.
- 对黑人患者来说",多发性溢出"的检测率下降了 (10.7%-11.6%).
结论:
- 胸部放射学基础模型显示了与性别和种族相关的重大偏见.
- 这些偏见导致患者子组的分类表现不同.
- 鉴定的偏差表明该模型可能不安全用于广泛的临床应用.
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