Promoting health equity through improved regulation of artificial intelligence medical devices
Open accessAbstract
Existing health disparities in the United States are partially driven by the way healthcare is delivered. There is interest in using Artificial Intelligence (AI)-driven software as medical devices (SaMD) to aid in healthcare delivery and reduce health disparities. However, AI-driven tools have the potential to codify bias in healthcare settings. Some AI-driven SaMDs have displayed substandard performance among racial and ethnic minorities. Auditing these tools for biased output can help produce more equitable outcomes across populations. However, there are currently no explicit Food and Drug Administration (FDA) regulations that examine bias in AI software in healthcare. Therefore, we propose the FDA develop a distinct regulatory process for AI-driven SaMDs that includes assessing equitable output across populations and avoiding potential health disparity exacerbation. This change could help prevent AI-driven health disparities nationwide.
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Many AI-driven SaMDs have displayed substandard performance among racial and ethnic minorities.
“However, while the evaluation criteria of SaMD focus on risk mitigation and the reproducibility and robustness from the software perspective, there are no explicit FDA regulations that examine fairness or bias in medical AI outputs, and indeed, many AI-driven SaMDs have displayed substandard performance among racial and ethnic minorities [113].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [113]
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References 30 · Cited by 14
Research works printed but not linked: 2
- "Aiming for Truth, Fairness, and Equity in Your Company's Use of AI." 2021. Federal Trade Commission. April 19, 2021. http://www.ftc.gov/business-guidance/blog/202 1/04/aiming-truth-fairness-equity-your-compan ys-use-ai. · DOI · Paper page
- "Using Artificial Intelligence and Algorithms." 2020. Federal Trade Commission. April 8, 2020. http://www.ftc.gov/business-guidance/blog/202 0/04/using-artificial-intelligence-algorithms. · DOI
- Authors
- Dortche, K., McCarthy, G., Banbury, S., & Yannatos, I.
- Affiliations
- Oregon Health & Science University (US)
- University of Pennsylvania (US)
- Venue
- Journal of Science Policy & Governance
- Published
- 2023-01-23
- Volume
- 21
- Issue
- 03
- ISSN
- 2372-2193 (ISSN-L)
- OpenAlex
- W4317749076
- Cited by
- 14
- References
- 30
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 2
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Science Policy & Governance (publishedVersion)
- Funders
- University of Pennsylvania
- Cleveland Clinic
- Cleveland Clinic Foundation
- 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 › Healthcare Regulation
- Keywords
- artificial intelligence, software as a medical device, health equity, health disparities, algorithmic bias, FDA regulation, regulatory policy, racial and ethnic minorities, algorithmic auditing, health care delivery
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