ReviewNPJ digital medicine2024
Addressing fairness issues in deep learning-based medical image analysis: a systematic review.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- PatchChestCT: A patch-level spatial annotation dataset for nine abnormalities in chest CT.Scientific data · 2026Article
- Diagnostic accuracy of deep learning-assisted low-dose CT screening for lung cancer detection: a systematic review and meta-analysis.Diagnostic and prognostic research · 2026Article
- State of abdominal CT datasets: A critical review of bias, clinical relevance, and real-world applicability.PLOS digital health · 2026Review
- AI-Assisted Chest X-Ray Interpretation in Resource-Limited Settings: LuAna Stepped-Wedge Trial Protocol.JMIR research protocols · 2026Article
- Test-fairness deep learning with influence score.PLOS digital health · 2026Article
- FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.NPJ digital medicine · 2026Article
- Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.Scientific reports · 2026Article
- Calibrated Deep-Learning Risk Indexing and Latent Behavioural Profiling for Occupational Mental-Health Risk Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Understanding AI risks from its characteristics and NMPA regulation perspectives.Insights into imaging · 2026Article
- Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer.Frontiers in immunology · 2026Review
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- Machine Learning Models Utilizing Oxidative Stress Biomarkers for Breast Cancer Prediction: Efficacy and Limitations in Sentinel Lymph Node Metastasis Detection.Biomedicines · 2025Article
- A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories.Cancers · 2025Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- FairICP: identifying biases and increasing transparency at the point of care in post-implementation clinical decision support using inductive conformal prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Equity-enhanced glaucoma progression prediction from OCT with knowledge distillation.NPJ digital medicine · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.
Identifiers
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.