Strategies to improve the impact of artificial intelligence on health equity: scoping review
Open accessAbstract
BACKGROUND: Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. OBJECTIVE: This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. METHODS: We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. RESULTS: In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. CONCLUSIONS: Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.
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The range of AI and machine learning methods projects PCORI is funding continues to grow and is directly responsive to the findings and recommendations of PCORI's Emerging Technologies and Therapeutics report-AI in Health Care: Strategies to Improve the Impact of AI on Health Equity.
“The range of AI/ML methods projects PCORI is funding continues to grow and is directly responsive to the findings and recommendations of PCORI’s Emerging Technologies and Therapeutics report—AI in Health Care: Strategies to Improve the Impact of AI on Health Equity.38,39”
From Advancing methodological development of artificial intelligence in patient-centered comparative clinical effectiveness research: Patient-Centered Outcomes Research Institute’s unique contribution to research done differently (2025) · cites this paper as [39]
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Artificial Intelligence in Healthcare: Advancing Innovation and Ethics to Foster Well-Being
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Pulido-Arias, D., & Arias-Mendoza, N. (2026) · Journal of Imaging · doi:10.3390/jimaging12040144 · Cited by 1 · see citing works
“This diagnostic inconsistency can directly impact patient stratification and therapeutic choices [7], creating an urgent clinical need for objective and reproducible tools that can support pathologists and standardize the diagnostic process [8].”
Ahmed, M. M., & Othman, Z. K. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s610208 · Cited by 1 · see citing works
“6 Without specifying which dimensions of transparency are being invoked and which actors bear responsibility, calls for transparency risk remaining aspirational rather than actionable in practice.”
Evaluation of Prospect and Challenges of Artificial Intelligence (AI) and Preparation for Future
Hossain, K. A. (2023) · American Journal of Computer Science and Technology · doi:10.11648/j.ajcst.20230604.13 · Cited by 1 · see citing works
Determinantes sociales de la salud mental: retórica, realidades y esperanzas
Alarcón, R. D. (2024) · Acta Herediana · doi:10.20453/ah.v67i1.5524 · Cited by 1 · see citing works
“Y el uso de recursos tecnológicos novísimos como inteligencia artificial (Berdahl et al., 2023) aguarda también enfoques razonables.”
BackgroundLacsa, J. E. M. (2024) · Journal of Public Health · doi:10.1093/pubmed/fdae123 · Cited by 1 · see citing works
Liu, H., Bîlc, M. I., & Ng, J. Y. (2025) · medRxiv · doi:10.64898/2025.12.09.25341890 · Cited by 1 · see citing works
Evaluation of Challenges for Extensive Use of Artificial Intelligence (AI) in Every Aspect of Life
Hossain, K. A. (2023) · American Journal of Computer Science and Technology · doi:10.11648/j.ajcst.20230604.11
Guleria, S., Guptill, J., Kumar, I., McClintic, M., & Rojas, J. C. (2024) · Research Square · doi:10.21203/rs.3.rs-4384505/v1
Creating Fairness—A Nexus Approach to Designing Healthcare Equity
Diserens, P., & Alafaireet, P. E. (2024) · Springer eBooks · doi:10.1007/978-3-031-65766-5_4
Centering Equity in Health AI: LLM Design Strategies for Impactful Solutions (Preprint)Preprint
Dewitt, A., Parker, A. G., Harrington, C., & Cole-Lewis, H. J. (2025) · doi:10.2196/preprints.71727
How multi-scale modeling can help examine social determinants of health and resulting disparities
Yoshida, K., Pienaar, E., Bynum, S. A., Chesler, N. C., Colebank, M. J., Heneghan, J. L., Tyus, N., Miller‐Kleinhenz, J. M., & Lee, B. Y. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013284
Role of governability and generative AI for healthcare
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Batakis, D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5462-0.ch003
Liu, H., Bîlc, M. I., & Ng, J. Y. (2025) · International Journal of Ayurveda Research · doi:10.4103/ijar.ijar_348_25
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Yadav, V. (2025) · International Conference on Sustainable Technologies for Humanity and Smart World (HSWTech) · doi:10.1109/hswtech64936.2025.11278156
“Active interaction between various sectors and publishing of open-source AI models at lower costs for equitable access may address the above disparities and inequalities [46, 36].”
Ethical Considerations in AI-Enabled Healthcare
Aldemir, E., Bloom, C., Honeyman, S. I., & Vardanyan, R. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_5
Implementation Science for AI Projects
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Introduction to the Opportunities, Risks, and Future Directions of AI in Pulmonary Medicine
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“…were frequently noted across the included studies, reflecting a well-recognized risk that AI systems can reproduce or exacerbate health inequities when trained on biased or unrepresentative data, or when deployed unevenly across resource-rich and resource-constrained settings [41, 44].”
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Recommended guidelines concerning artificial intelligence and health equity: a scoping review
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The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists
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Equity-Aware Agentic AI Architectures and Governance With SDG-Aligned Healthcare Innovation
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- Authors
- Berdahl, C. T., Baker, L., Mann, S., Osoba, O., & Girosi, F.
- Affiliations
- Cedars-Sinai Medical Center (US)
- RAND Corporation (US)
- Corresponding author
- Berdahl, Carl Thomas
- Venue
- JMIR AI
- Publisher
- JMIR Publications Inc.
- Published
- 2023-02-07
- Volume
- 2
- Pages
- e42936
- ISSN
- 2817-1705 (ISSN-L)
- PMC
- PMC11041459
- OpenAlex
- W4319347179
- Cited by
- 68
- References
- 45
- Citations by year
- 2026: 21 · 2025: 29 · 2024: 12 · 2023: 6
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- JMIR AI (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Patient-Centered Outcomes Research Institute
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, health equity, algorithmic bias, health disparities, AI governance, data quality, model transparency, COVID-19 pandemic
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