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
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
Massachusetts Institute of Technology（麻省理工学院）；University at Buffalo（布法罗大学）
Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；University of Tübingen（蒂宾根大学）；University of Adelaide（阿德莱德大学）；Stanford University（斯坦福大学）
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has found that such classifiers can exhibit biases in the form of gaps in predictive performance across protected groups. In this paper, we question whether striving to achieve zero disparities in predictive performance (i.e. group fairness) is the appropriate fairness definition in the clinical setting, over minimax fairness, which focuses on maximizing the performance of the worst-case group. We benchmark the performan…
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Queen Mary University of London（伦敦玛丽女王大学）；National Technical University of Athens（雅典国家技术大学）；National Infrastructures for Research and Technology（国家研究与技术基础设施机构）
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making in healthcare. Fair machine learning has focused on mitigating such biases against disadvantaged or marginalised group…
DUCX: Decomposing Unfairness in Tool-Using Chest X-ray Agents
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Anhui, China（人工智能研究所，合肥国家科学中心，安徽，中国）；The University of British Columbia, Vancouver, BC V6Z 1Z4, Canada（不列颠哥伦比亚大学，温哥华，BC V6Z 1Z4，加拿大）；Vector Institute, Toronto, ON M5G 1M1, Canada（向量研究所，多伦多，ON M5G 1M1，加拿大）
Subgroup performance analysis of adaptation strategies for chest X-ray foundation models
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Causality in Healthcare AI Hub（医疗保健AI因果关系中心）；National Heart & Lung Institute（国家心肺研究所）
Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
University of Science, Ho Chi Minh City（胡志明市科学大学）；Vietnam National University, Ho Chi Minh City（胡志明市越南国立大学）；VinUni-Illinois Smart Health Center, VinUniversity（VinUni-伊利诺伊智能健康中心，Vin大学）；Carnegie Mellon University（卡内基梅隆大学）；Northwestern University（西北大学）；College of Engineering & Computer Science, VinUniversity（Vin大学工程与计算机科学学院）
National Tsing Hua University（国立清华大学）；Massachusetts Institute of Technology（麻省理工学院）；Emory University（埃默里大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Emory University School of Medicine（埃默里大学医学院）
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing. Currently, no research applies deep learning (DL) algorithms that use large multi-si…
Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
National Taiwan University Hospital, Taiwan（台湾国立台湾大学医院）；University of Toronto, Canada（加拿大多伦多大学）；York University, Canada（加拿大约克大学）；MIT Critical Data, United States of America（美国MIT Critical Data）；Massachusetts Institute of Technology, USA（美国麻省理工学院）；Mass General Brigham, USA（美国麻省总医院）；Dana-Farber Cancer Institute, USA（美国达纳-法伯癌症研究所）
A Generative Foundation Model for Chest Radiography
Department of Radiation Oncology, Stanford University School of Medicine（斯坦福大学医学院放射肿瘤科）；Department of Computer Science, School of Computing and Data Science, The University of Hong Kong（香港大学计算机科学系）；Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University（康奈尔大学兽医学院人口医学与诊断科学系）；Department of Radiology, The First Affiliated Hospital of Jinan University（暨南大学第一附属医院放射科）；Department of Radiology, Stanford University School of Medicine（斯坦福大学医学院放射科）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
(Predictable) Performance Bias in Unsupervised Anomaly Detection
Klinikum rechts der Isar der Technischen Universität München（慕尼黑工业大学 rechts der Isar 医院）；Technical University of Munich（慕尼黑工业大学）；Konrad Zuse School of Excellence in Reliable AI（康拉德·楚泽可靠人工智能卓越学院）；Munich Data Science Institute（慕尼黑数据科学研究所）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Imperial College London（帝国理工学院）
FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis
University of Pittsburgh, Swanson School of Engineering（匹兹堡大学斯旺森工程学院）；The University of North Carolina at Chapel Hill, Department of Computer Science（北卡罗来纳大学教堂山分校计算机科学系）；Arizona State University, School of Computing and Augmented Intelligence（亚利桑那州立大学计算与增强智能学院）；University of Pittsburgh, Department of Psychiatry（匹兹堡大学精神病学系）
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Center for Artificial Intelligence in Medicine and Imaging, Stanford University（斯坦福大学人工智能医学与成像中心）；Department of Radiology, Stanford University（斯坦福大学放射科）；Department of Applied Physics, Stanford University（斯坦福大学应用物理系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich（苏黎世大学医院诊断与介入放射学研究所）；LAION；Juelich Supercomputing Center (JSC), Research Center Juelich (FZJ)（耶鲁超级计算中心（JSC）、耶鲁研究中心（FZJ））；Department of Radiology & Imaging Sciences, Emory University（埃默里大学放射科与成像科学系）；Department of Radiology, University of Florida College of Medicine（佛罗里达大学医学院放射科）
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Klinikum rechts der Isar（Isar临床中心）；TUM School of Medicine and Health（慕尼黑工业大学医学与健康学院）；Institut fuer KI und Informatik in der Medizin（医学人工智能与信息学研究所）；German National Cohort（德国国家队列研究中心）
FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
Harvard Medical School（哈佛医学院）；New York University（纽约大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）
