Treating health disparities with artificial intelligence
Integrity signals
Registry
Cites
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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 be systematically biased toward different outputs, which may lead to different or even unfair treatment, on the basis of extant features.
“It has been shown that AI systems may be systematically biased toward different outputs, which may lead to different or even unfair treatment, on the basis of extant features53,57–59.”
From Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020) · cites this paper as [59]
AI offers opportunities to mitigate healthcare disparities that are already known to exist.
“Conversely, AI offers opportunities to mitigate healthcare disparities that are already known to exist (174, 175).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [174]
The citation lists haven’t been fetched yet
References 20 · Cited by 235
- Authors
- Chen, I. Y., Joshi, S., & Ghassemi, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Vector Institute (CA)
- University of Toronto (CA)
- Corresponding author
- Chen, Irene Y.
- Venue
- Nature Medicine
- Published
- 2020-01
- Volume
- 26
- Issue
- 1
- Pages
- 16-17
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3000127051
- Cited by
- 235
- References
- 20
- Citations by year
- 2026: 43 · 2025: 45 · 2024: 29 · 2023: 62 · 2022: 16 · 2021: 21 · 2020: 19
- FWCI
- 8.69
- Type
- journal article
- Language
- en
- Open access
- closed
- 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
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- health disparities, artificial intelligence in healthcare, algorithmic bias, algorithmic fairness, health equity, algorithmic auditing, algorithmic transparency
- MeSH terms
- Clinical Decision-Making, Algorithms, Artificial Intelligence, Humans, Healthcare Disparities
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.
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