mlsanityData to 7 Oct 2026

Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications

arXiv 2407.16953 ↗First posted 24 Jul 2024cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Recently, the research community of computerized medical imaging has started to discuss and address potential fairness issues that may emerge when developing and deploying AI systems for medical image analysis. This chapter covers some of the pressing challenges encountered when doing research in this area, and it is intended to raise questions and provide food for thought for those aiming to enter this research field. The chapter first discusses various sources of bias, including data collection, model training, and clinical deployment, and their impact on the fairness of machine learning algorithms in medical image computing. We then turn to discussing open challenges that we believe require attention from researchers and practitioners, as well as potential pitfalls of naive application of common methods in the field. We cover a variety of topics including the impact of biased metrics when auditing for fairness, the leveling down effect, task difficulty variations among subgroups, discovering biases in unseen populations, and explaining biases beyond standard demographic attributes.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

20 of 43 references.

References · 20

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,232
  3. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  4. 2022A Survey of Vision-Language Pre-Trained Modelscited by 177
  5. 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 155
  6. 2022Better Uncertainty Calibration via Proper Scores for Classification and Beyondcited by 65
  7. 2021Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentationcited by 57
  8. 2023Understanding metric-related pitfalls in image analysis validationcited by 57
  9. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  10. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  11. 2023The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by defaultcited by 36
  12. 2017Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truthcited by 35
8 more
  1. 2021Estimating and Improving Fairness with Adversarial Learningcited by 26
  2. 2022Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigationcited by 11
  3. 2023FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensemblingcited by 11
  4. 2023Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentationcited by 8
  5. 2023Towards unraveling calibration biases in medical image analysiscited by 7
  6. 2023Are demographically invariant models and representations in medical imaging fair?cited by 6
  7. 2020Measuring Diversity of Artificial Intelligence Conferencescited by 5
  8. 2021Two-step adversarial debiasing with partial learning -- medical image case-studiescited by 5

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025BM-CL: Bias Mitigation through the lens of Continual Learningcited by 0
  2. 2025Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structurescited by 0