Mitigating bias in clinical machine learning models
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Bias and unequal performance across demographic groups remain significant concerns.
“Bias and unequal performance across demographic groups remain significant concerns [21,22].”
Predictive models are trained on historical data that may reflect structural inequalities.
“predictive models are trained on historical data that may reflect structural inequalities [22].”
Measurement and aggregation bias occurs when AI models adopt a one-size-fits-all approach, assuming population averages are sufficient to address subgroup-specific variations.
“Measurement and aggregation bias (n = 6) was one of the most frequently identified biases in this phase, as many AI models adopt a “one-size-fits-all” approach, assuming population averages are sufficient to address subgroup-specific variations [52,53].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [53]
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Sorted by citations
Amaya-Santos, S., Vargas, C., & Bermúdez-Tamayo, C. (2026) · Public Health in Practice · doi:10.1016/j.puhip.2025.100713 · Cited by 4 · see citing works
“Measurement and aggregation bias (n = 6) was one of the most frequently identified biases in this phase, as many AI models adopt a “ one-size-fits-all ” approach, assuming population averages are sufficient to address subgroup-specific variations [52,53].”
Patil, S. S., Jha, G. K., Mohsin, N., Patel, P., Ralte, L., & Shukla, A. K. (2026) · Cureus · doi:10.7759/cureus.109905
“Bias and unequal performance across demographic groups remain significant concerns [21,22].”
- Authors
- Perez-Downes, J. C., Tseng, A. S., McConn, K. A., Elattar, S. M., Sokumbi, O., Sebro, R. A., Allyse, M. A., Dangott, B. J., Carter, R. E., & Adedinsewo, D.
- Affiliations
- Mayo Clinic in Florida (US)
- Mayo Clinic (US)
- Corresponding author
- Adedinsewo, Demilade
- ORCID
- Venue
- Current Treatment Options in Cardiovascular Medicine
- Published
- 2024-02-10
- Volume
- 26
- Issue
- 3
- Pages
- 29-45
- ISSN
- 1092-8464 (ISSN-L)
- 1534-3189
- OpenAlex
- W4391722986
- Cited by
- 23
- References
- 65
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 4
- FWCI
- 7.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Current Treatment Options in Cardiovascular Medicine (publishedVersion)
- Funders
- National Center for Advancing Translational Sciences (UL1 TR002377)
- National Institutes of Health (K12 HD065987)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- algorithmic bias, machine learning in healthcare, bias mitigation, health equity, ethical considerations, clinical decision support, model generalization, responsible AI development
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