ReviewPatterns (New York, N.Y.)2023
The path toward equal performance in medical machine learning.
Review in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT.European radiology · 2026Article
- Is artificial intelligence a friend or foe to epidemiology?Annals of epidemiology · 2026Review
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- Connecting algorithmic fairness and fair outcomes in a sociotechnical simulation case study of AI-assisted healthcare.Nature communications · 2025Article
- Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph.Journal of clinical medicine · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- The Permissibility of Biased AI in a Biased World: An Ethical Analysis of AI for Screening and Referrals for Diabetic Retinopathy in Singapore.Asian bioethics review · 2025Article
- FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare.Patterns (New York, N.Y.) · 2024Article
- The limits of fair medical imaging AI in real-world generalization.Nature medicine · 2024Article
- Deep learning with noisy labels in medical prediction problems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study.EClinicalMedicine · 2024Article
- An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features.Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ... · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To ensure equitable quality of care, differences in machine learning model performance between patient groups must be addressed. Here, we argue that two separate mechanisms can cause performance differences between groups. First, model performance may be worse than theoretically achievable in a given group. This can occur due to a combination of group underrepresentation, modeling choices, and the characteristics of the prediction task at hand. We examine scenarios in which underrepresentation leads to underperformance, scenarios in which it does not, and the differences between them. Second, the optimal achievable performance may also differ between groups due to differences in the intrinsic difficulty of the prediction task. We discuss several possible causes of such differences in task difficulty. In addition, challenges such as label biases and selection biases may confound both learning and performance evaluation. We highlight consequences for the path toward equal performance, and we emphasize that leveling
Identifiers
What OpenQuestion 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 OpenQuestion graph.