mlsanityData to 14 Oct 2026

A Review of Causality for Learning Algorithms in Medical Image Analysis

arXiv 2206.05498 ↗First posted 11 Jun 2022cs.CV cs.AI cs.GLcited by 18

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Abstract (arXiv)

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms. We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 47 of its 86 references that are in the map. First posted on arXiv 11 Jun 2022.

Builds on

47 of 86 references.

References · 47

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35 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 8; 2024: 3; 2025: 4; 2026: 2.

18 papers.

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  1. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  2. 2024Benchmarking Counterfactual Image Generationcited by 22
  3. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  4. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  5. 2024Causal Agent based on Large Language Modelcited by 10
  6. 2023A Framework for Interpretability in Machine Learning for Medical Imagingcited by 9
  7. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  8. 2023Are demographically invariant models and representations in medical imaging fair?cited by 6
  9. 2025Causally Steered Diffusion for Automated Video Counterfactual Generationcited by 6
  10. 2023Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Modelscited by 5
  11. 2022A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigmscited by 4
  12. 2023Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: a Case Study on Hateful Memescited by 2
6 more
  1. 2023Pretrained Vision Models for Predicting High-Risk Breast Cancer Stagecited by 1
  2. 2025CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomographycited by 1
  3. 2023Causal Image Synthesis of Brain MR in 3Dcited by 0
  4. 2025Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Frameworkcited by 0
  5. 2026Causal Transfer in Medical Image Analysiscited by 0
  6. 2026To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentationcited by 0