ArticleNature communications2023
Improving model fairness in image-based computer-aided diagnosis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.IEEE transactions on medical imaging · 2026Article
- Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.Ophthalmology science · 2026Article
- Understanding pre-training data effects in retinal foundation models using two large fundus cohorts.Nature communications · 2026Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.Medical image analysis · 2025Article
- Uncertainty-aware large language models for explainable disease diagnosis.NPJ digital medicine · 2025Article
- Article
- A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.Nature communications · 2025Article
- Demographic bias of expert-level vision-language foundation models in medical imaging.Science advances · 2025Article
- Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.Nature communications · 2025Article
- Unveiling the power of language models in chemical research question answering.Communications chemistry · 2025Article
- Artificial Intelligence in Medicine and Imaging Applications.Current pharmaceutical design · 2025Review
- Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.ArXiv · 2024Article
- Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ digital medicine · 2024Review
- The limits of fair medical imaging AI in real-world generalization.Nature medicine · 2024Article
- Improving Fairness of Automated Chest Radiograph Diagnosis by Contrastive Learning.Radiology. Artificial intelligence · 2024Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
- Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Despite this concern, the problem of improving model fairness in medical image classification by deep learning has yet to be fully studied. To address this issue, we propose an algorithm that leverages the marginal pairwise equal opportunity to reduce bias in medical image classification. Our evaluations across four tasks using four independent large-scale cohorts demonstrate that our proposed algorithm not only improves fairness in individual and intersectional subgroups but also maintains overall performance. Specifically, the relative change in pairwise fairness difference between our proposed model and the baseline model was reduced by over 35%, while the relative change in AUC value was typically within 1%. By reducing the bias generated by deep learning models, our proposed approach can potentially alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.