Machine learning systems have received much attention recently for their
ability to achieve expert-level performance on clinical tasks, particularly in
medical imaging. Here, we examine the extent to which state-of-the-art deep
learning classifiers trained to yield diagnostic labels from X-ray images are
biased with respect to protected attributes. We train convolution neural
networks to predict 14 diagnostic labels in 3 prominent public chest X-ray
datasets: MIMIC-CXR, Chest-Xray8, CheXpert, as well as a multi-site aggregation
of all those datasets. We evaluate the TPR disparity -- the difference in true
positive rates (TPR) -- among different protected attributes such as patient
sex, age, race, and insurance type as a proxy for socioeconomic status. We
demonstrate that TPR disparities exist in the state-of-the-art classifiers in
all datasets, for all clinical tasks, and all subgroups. A multi-source dataset
corresponds to the smallest disparities, suggesting one way to reduce bias. We
find that TPR disparities are not significantly correlated with a subgroup's
proportional disease burden. As clinical models move from papers to products,
we encourage clinical decision makers to carefully audit for algorithmic
disparities prior to deployment. Our code can be found at,
this https URL

在医学影像方面，机器学习系统已经受到了广泛的关注。本文检查了最先进的深度学习分类器在从 X 光图像中产生诊断标签方面的性能偏差程度，发现在所有数据集，所有临床任务和所有子群体中都存在 TPR 差异。

深度胸透分类器中的公平差距

CheXclusion: Fairness gaps in deep chest X-ray classifiers

In this paper, we explore the possibility to apply machine learning to make
diagnostic predictions using discomfort drawings. A discomfort drawing is an
intuitive way for patients to express discomfort and pain related symptoms.
These drawings have proven to be an effective method to collect patient data
and make diagnostic decisions in real-life practice. A dataset from real-world
patient cases is collected for which medical experts provide diagnostic labels.
Next, we use a factorized multimodal topic model, Inter-Battery Topic Model
(IBTM), to train a system that can make diagnostic predictions given an unseen
discomfort drawing. The number of output diagnostic labels is determined by
using mean-shift clustering on the discomfort drawing. Experimental results
show reasonable predictions of diagnostic labels given an unseen discomfort
drawing. Additionally, we generate synthetic discomfort drawings with IBTM
given a diagnostic label, which results in typical cases of symptoms. The
positive result indicates a significant potential of machine learning to be
used for parts of the pain diagnostic process and to be a decision support
system for physicians and other health care personnel.

应用机器学习方法，使用患者不适图形作为数据，使用 Inter-Battery Topic Model (IBTM) 训练系统进行疾病预测，并利用均值漂移聚类确定标签数量，最终得出合理的预测结果，显示出机器学习在疼痛诊断流程中有重要的潜在应用价值。