Bias and ethical considerations in machine learning and the automation of perioperative risk assessment
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AI solutions have led to inadvertent bias of modeling results, with increasing concerns and related examples across diverse populations.
“As AI becomes more pervasive in both public and personal health across diverse populations, there have been increasing concerns, and related examples, of AI solutions leading to inadvertent bias of modeling results (45–48).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [47]
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Accessing Artificial Intelligence for Clinical Decision-Making
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- Authors
- O'Reilly-Shah, V. N., Gentry, K. R., Walters, A. M., Zivot, J., Anderson, C. T., & Tighe, P. J.
- Affiliations
- Seattle Children's Hospital (US)
- University of Washington (US)
- Emory University (US)
- University of Florida Health (US)
- Venue
- British Journal of Anaesthesia
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 125
- Issue
- 6
- Pages
- 843-846
- ISSN
- 0007-0912 (ISSN-L)
- PMC
- PMC7442146
- OpenAlex
- W3081377657
- Cited by
- 62
- References
- 25
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 13 · 2023: 9 · 2022: 13 · 2021: 8 · 2020: 1
- FWCI
- 9.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- British Journal of Anaesthesia (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning bias, ethical considerations, perioperative risk assessment, algorithmic fairness
- MeSH terms
- Machine Learning, Anesthesiology, Automation, Humans, Bias, Risk Assessment, Perioperative Care
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