ArticleRadiology. Artificial intelligence2023
Risk of Bias in Chest Radiography Deep Learning Foundation Models.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- Evaluating sex bias in artificial intelligence-based echocardiographic analysis: a retrospective study on individuals with normal cardiac function and patients with systolic and diastolic dysfunction.Cardiovascular ultrasound · 2026Article
- Explainable AI for chest radiographs: sex-stratified fairness auditing in CNN-based pneumonia detection.Scientific reports · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Photoacoustic device fingerprints induce bias in deep learning models.Scientific reports · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Comparative Evaluation of Deep Learning and Foundation Model Embeddings for Osteoarthritis Feature Classification in Knee Radiographs.Journal of imaging informatics in medicine · 2026Article
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- Article
- Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.Frontiers in radiology · 2026Article
- Cross-modal bias in medical vision-language models: a pipeline-aware framework for mechanisms, evaluation, and mitigation.Frontiers in digital health · 2026Review
- Extended Roles in Healthcare Delivery: What Is the Role of the Laboratory in Addressing Ethnicity-Related Healthcare Disparities?Diagnostics (Basel, Switzerland) · 2025Article
- EVA-X: a foundation model for general chest x-ray analysis with self-supervised learning.NPJ digital medicine · 2025Article
- Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.Research square · 2025Article
- Radiological Findings of Chest X-Rays During the Hajj Seasons 1444-1445 H/2023-2024 G: Diagnostic Quality and Gender Differences in Interpretation Concordance.International journal of environmental research and public health · 2025Article
- Detection of pneumonia in children through chest radiographs using artificial intelligence in a low-resource setting: A pilot study.PLOS digital health · 2025Article
- Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.Insights into imaging · 2025Review
- DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributes.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To analyze a recently published chest radiography foundation model for the presence of biases that could lead to subgroup performance disparities across biologic sex and race. Materials and Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study used 127 118 chest radiographs from 42 884 patients (mean age, 63 years ± 17 [SD]; 23 623 male, 19 261 female) from the CheXpert dataset that were collected between October 2002 and July 2017. To determine the presence of bias in features generated by a chest radiography foundation model and baseline deep learning model, dimensionality reduction methods together with two-sample Kolmogorov-Smirnov tests were used to detect distribution shifts across sex and race. A comprehensive disease detection performance analysis was then performed to associate any biases in the features to specific disparities in classification performance across patient subgroups. Results: Ten of 12 pairwise comparisons across biologic sex and race showed statistically significant differences in the studied foundation model, compared with four significant tests in the baseline model. Significant differences were found between male and female ( Conclusion: The studied chest radiography foundation model demonstrated racial and sex-related bias, which led to disparate performance across patient subgroups; thus, this model may be unsafe for clinical applications.
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