Medical imaging data science competitions should report dataset demographics and evaluate for bias
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI systems may produce outputs that are skewed by biases related to race, ethnicity, sex, religion, politics, and other factors.
“AI systems may also produce outputs that are skewed by biases related to race, ethnicity, sex, religion, politics, and other factors. [5, 23, 53–56]”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [54]
Racial and ethnic biases impact the use of AI/ML in medical imaging, diagnosis, and prognosis due to biases in healthcare databases.
“Studies have shown that racial and ethnic biases impact the use of AI/ML in medical imaging, diagnosis, and prognosis due to biases in healthcare databases [78, 154].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [78]
The citation lists haven’t been fetched yet
References 8 · Cited by 26
- Authors
- Garin, S. P., Parekh, V. S., Sulam, J., & Yi, P. H.
- Affiliations
- University of Maryland, Baltimore (US)
- Johns Hopkins University (US)
- Corresponding author
- Yi, Paul H.
- Venue
- Nature Medicine
- Published
- 2023-04-03
- Volume
- 29
- Issue
- 5
- Pages
- 1038-1039
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4362523852
- Cited by
- 26
- References
- 8
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 11 · 2023: 4
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic bias, dataset documentation, bias assessment
- MeSH terms
- Data Science, Demography, Diagnostic Imaging, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
