ArticleProceedings of the ACM Conference on Health, Inference, and Learning2020
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.
Article in Proceedings of the ACM Conference on Health, Inference, and Learning, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers, 3 of them syntheses that pooled it.
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
154 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.Breast cancer research and treatment · 2024Pooled it
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.Nature medicine · 2020Guideline
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.Nature medicine · 2020Guideline
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Refined Exclusion in Medical AI: Data Justice and Patient Safety Governance.Journal of medical Internet research · 2026Article
- CXR-LT 2026 challenge: Multi-center long-tailed and zero shot chest X-ray classification.Medical image analysis · 2026Article
- Article
- Deep Learning for Schatzker Classification on Anteroposterior Radiographs: A Controlled Benchmark and a Transferable Control Protocol.Journal of clinical medicine · 2026Article
- An artificial intelligence framework for universal landmark matching and morphometry in musculoskeletal radiography.European radiology · 2026Article
- Artificial intelligence for postoperative plain radiographic assessment after reverse total shoulder arthroplasty: current evidence, clinical readiness, and limitations.Clinics in shoulder and elbow · 2026Article
- Beyond the Black Box: Is Artificial Intelligence Ready to Reshape Neurosurgical Decision-Making? A Narrative Review.Health science reports · 2026Article
- Continual Learning in Medical Image Analysis: Appropriate Use and Lifecycle Governance.Journal of the Korean Society of Radiology · 2026Review
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- There is no free benchmark: An institutional view of legal AI benchmarking.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.The Lancet. Digital health · 2026Article
- Predicting diffusion-FLAIR mismatch from B1000 and ADC without FLAIR: A deep learning-based approach.Scientific reports · 2026Observational
- Predictive performance precision analysis in medicine: identification of low-confidence predictions at patient and profile levels (MED3pa I).Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Effect of Irreversible Compression on the Pulmonary Nodule Detection Rate in Chest Radiographs Using AI Software.Diagnostics (Basel, Switzerland) · 2026Article
94 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 2 countries.
Funding
Abstract
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but the model may still consistently miss a rare but aggressive cancer subtype. We refer to this problem as
Indexed as
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.