Equity and Artificial Intelligence in Surgical Care
Open accessAbstract
This Viewpoint endeavors to provide an overview of mechanisms by which artificial intelligence can either propagate or counteract disparities and suggests methods to tilt the balance toward fairness and equity in surgical care.
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AI-enabled DHTs can amplify implicit bias and discrimination if trained on data that reflect the health-care disparities experienced by groups defined by race, ethnicity, gender, sexual orientation, socioeconomic status, or geographic location.
“Moreover, AI-enabled DHTs can amplify implicit bias and discrimination if trained on data that reflect the health-care disparities experienced by groups defined by race, ethnicity, gender, sexual orientation, socioeconomic status, or geographic location.126”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [126]
The “Artificial Intelligence can Propagate Disparities” section says algorithms trained on biased data produce biased results, and the Summary says surgical decision-support tools propagate disparities when source data and input features are biased. The paper discusses race, ethnicity, sex, gender, socioeconomic factors, and geography, but the supplied passages do not address sexual orientation; its discussion is also specific to surgical AI rather than AI-enabled digital health technologies generally. (LLM, conf=0.84)
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- Authors
- Johnson-Mann, C. N., Loftus, T. J., & Bihorac, A.
- Affiliations
- University of Florida Health (US)
- Venue
- JAMA Surgery
- Publisher
- American Medical Association (AMA)
- Published
- 2021-06-01
- Volume
- 156
- Issue
- 6
- Pages
- 509
- ISSN
- 2168-6254 (ISSN-L)
- PMC
- PMC8273554
- OpenAlex
- W3133277548
- Cited by
- 44
- References
- 8
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 6 · 2023: 7 · 2022: 6 · 2021: 2
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA Surgery (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (K23GM140268, P50GM111152, R01GM110240)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Keywords
- artificial intelligence, health equity, health disparities, surgical care, algorithmic fairness, algorithmic bias
- MeSH terms
- Clinical Decision-Making, Bias, Implicit, Artificial Intelligence, Humans, Surgical Procedures, Operative, Patient Selection, Healthcare Disparities
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