Advancing health equity with artificial intelligence
Open accessAbstract
Population and public health are in the midst of an artificial intelligence revolution capable of radically altering existing models of care delivery and practice. Just as AI seeks to mirror human cognition through its data-driven analytics, it can also reflect the biases present in our collective conscience. In this Viewpoint, we use past and counterfactual examples to illustrate the sequelae of unmitigated bias in healthcare artificial intelligence. Past examples indicate that if the benefits of emerging AI technologies are to be realized, consensus around the regulation of algorithmic bias at the policy level is needed to ensure their ethical integration into the health system. This paper puts forth regulatory strategies for uprooting bias in healthcare AI that can inform ongoing efforts to establish a framework for federal oversight. We highlight three overarching oversight principles in bias mitigation that maps to each phase of the algorithm life cycle.
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Deployed AI models for clinical decisions like the ESM can inappropriately increase triage and trigger unnecessary diagnostic testing and prescriptions, decreasing the quality of clinical care and increasing hospital costs.
“Deployed AI models for clinical decisions like the ESM can inappropriately increase triage and trigger unnecessary diagnostic testing and prescriptions, decreasing the quality of clinical care and increasing hospital costs [101].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [101]
Interpretability can detect bias by examining how the features that drive model predictions differ for patients in different subgroups.
“Interpretability can also detect bias by examining how the features that drive model predictions differ for patients in different subgroups (which may be differentially aligned with best care practices) [101].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [101]
A model that exhibits predictive parity may not have equalized odds, and which metric is more important to optimize depends on the intended clinical use-case.
“For example, a model that exhibits predictive parity (no difference in precision across groups) may not have equalized odds (similar sensitivity and specificity across groups), which metric is more important to optimize on depends on the intended clinical use-case [101].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [101]
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Self-citationData access, data bias, data equity
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- Authors
- Thomasian, N. M., Eickhoff, C., & Adashi, E. Y.
- Affiliations
- Harvard University (US)
- Brown University (US)
- Corresponding author
- Thomasian, Nicole M.
- Venue
- Journal of Public Health Policy
- Published
- 2021-11-22
- Volume
- 42
- Issue
- 4
- Pages
- 602-611
- ISSN
- 0197-5897 (ISSN-L)
- 1745-655X
- PMC
- PMC8607970
- OpenAlex
- W3215517669
- Cited by
- 117
- References
- 35
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 37 · 2023: 25 · 2022: 5 · 2021: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Public Health Policy (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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 › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in healthcare, algorithmic bias, health equity, algorithmic fairness, bias mitigation, AI regulation, algorithmic oversight, ethical AI, federal oversight, health policy
- MeSH terms
- Health Equity, Artificial Intelligence, Delivery of Health Care, Humans, Policy
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