CheXclusion: Fairness gaps in deep chest X-ray classifiers (2003.00827v2)

Abstract: 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, https://github.com/LalehSeyyed/CheXclusion

Analysis of Fairness Gaps in Deep Chest X-ray Classifiers

The paper "CheXclusion: Fairness gaps in deep chest X-ray classifiers" by Laleh Seyyed-Kalantari et al. presents a focused evaluation of bias in state-of-the-art deep learning models used for classifying chest X-ray images. More specifically, the authors investigate the extent of disparities in the performance of these classifiers across various protected attributes such as sex, age, race, and insurance type. The paper is conducted across three large and prominent public chest X-ray datasets: MIMIC-CXR, Chest-Xray8, and CheXpert. Additionally, the authors construct a multi-source dataset by aggregating these datasets to further examine bias reduction possibilities.

Summary of Methodologies and Results

The authors employ convolutional neural networks (CNNs), initializing them with pre-trained weights from ImageNet to predict probabilities across 14 diagnostic labels. By observing the disparity in true positive rates (TPR) for different subgroups, the paper identifies and quantifies biases in each dataset. The authors report significant TPR disparities that suggest systematic biases against certain subgroups, with consistent patterns of unfavorable outcomes for minority, female, and younger patients.

Notably, statistical analysis reveals that the TPR disparities are not always correlated significantly with a subgroup's proportional representation in the dataset for most attributes and datasets. This challenges the assumption that increasing subgroup size in the dataset can inherently mitigate bias. Moreover, the multi-source dataset consistently shows smaller TPR disparities, hinting at the potential benefits of using more comprehensive data in model training to enhance fairness.

Implications and Future Directions

The findings from this research bear important implications for the deployment of AI models in clinical settings. The pronounced disparities identified underscore the ethical and clinical risks of relying on these classifiers without thorough fairness audits. This work suggests that mere accuracy on generalized datasets does not equate to equitable model outputs across diverse patient groups, highlighting the necessity for fairness-centric evaluation metrics in clinical AI model validation.

Practically, the paper calls for clinical decision-makers to critically assess algorithmic biases before deploying AI models in healthcare settings. The observed decrease in bias when using a multi-source dataset implies that broader and more diverse data collection during dataset creation might be an essential step towards mitigating inherent biases in training datasets.

Theoretically, the paper enriches the ongoing discourse on fairness in AI, especially concerning medical applications. It argues against simplistic correlations between data balance and fairness, suggesting that nuanced techniques and sophisticated fairness interventions are required.

Speculation on Future Developments in AI

Looking ahead, this paper lays the groundwork for future research exploring algorithmic debiasing techniques in medical imaging. It opens avenues for more comprehensive studies that could integrate fairness-aware machine learning paradigms and advanced bias-correction methodologies. Moreover, as the field continues to grow, the development of standardized fairness auditing frameworks could play a pivotal role in shaping the future landscape of AI in healthcare.

The research presented in "CheXclusion: Fairness gaps in deep chest X-ray classifiers" serves as a crucial reminder that technical excellence must be complemented by ethical responsibility. As AI systems become further entrenched in clinical workflows, attention to fairness and equity will be integral to realizing their full potential in improving healthcare outcomes.