Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact

Shama Patel; Katherine O. Robsky; Wenhui Mao; Umar Usman; Lorena Garcia; Olubukola Omobowale; Gugu Mchunu; Joowhan Sung; Gari Clifford; Anant Madabhushi; Azra Ismail; Saara Bidiwala; Seemab Mehmood; Christine Ngaruiya; Paul E. Kilgore; Keith Martin

doi:10.5334/aogh.5268

Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact

Volume 92 (2026): Issue 1

By: Shama Patel, Katherine O. Robsky, Wenhui Mao, Umar Usman, Lorena Garcia, Olubukola Omobowale, Gugu Mchunu, Joowhan Sung, Gari Clifford, Anant Madabhushi, Azra Ismail, Saara Bidiwala, Seemab Mehmood, Christine Ngaruiya, Paul E. Kilgore and Keith Martin

Open Access

|Jun 2026

Abstract

Artificial Intelligence (AI) is transforming health worldwide, yet its benefits remain unevenly distributed and insufficiently evaluated in real‑world settings. Drawing on a structured narrative landscape review conducted by the Consortium of Universities of Global Health Research Committee’s AI working group and deliberations from its 2025 pre‑conference session (171 registrants), we synthesize AI applications across education, epidemiology, and clinical medicine, with an emphasis on data equity in global health. The review drew on peer‑reviewed and gray literature across these domains and was synthesized thematically to identify implementation barriers, governance challenges, and equity implications. In conjunction with illustrative case studies, we identify five strategic imperatives: contextualized governance frameworks, equitable capacity‑building, rigorous implementation and cost‑effectiveness research, open knowledge repositories, and community‑centered ethical design. We argue that AI must shift from technological optimism to locally led, evidence‑driven, and equity‑centered deployment. Without these paradigm shifts, AI risks reinforcing the very inequities it aims to solve.

References

Wahl B, Cossy‑Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: How can AI contribute to health in resource‑poor settings? BMJ Glob Health. 2018;3(4):e000798. doi:10.1136/bmjgh-2018-000798.
Open DOI Search in Google Scholar Back to article
Muralidharan V, Ng MY, AlSalamah S, et al. Global Initiative on AI for health (GI‑AI4H): Strategic priorities advancing governance across the United Nations. NPJ Digit Med. 2025;8(1):219. doi:10.1038/s41746-025-01618-x.
Open DOI Search in Google Scholar Back to article
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579–1586. doi:10.1016/S0140-6736(20)30226-9.
Open DOI Search in Google Scholar Back to article
Freeman S, Wang A, Saraf S, et al. Developing an AI governance framework for safe and responsible AI in health care organizations: Protocol for a multimethod study. JMIR Res Protoc. 2025;14(1):e75702. doi:10.2196/75702.
Open DOI Search in Google Scholar Back to article
Hassan M, Borycki EM, Kushniruk AW. Artificial intelligence governance framework for healthcare. Healthc Manage Forum. 2025;38(2):125–130. doi:10.1177/08404704241291226.
Open DOI Search in Google Scholar Back to article
Barzelay A, Ng J, Romanoff M. Governing artificial intelligence responsibility in low to middle income countries: Enabling pathways to sustainable development. Calif West Int Law J. 2024;54(2):415–463. https://scholarlycommons.law.cwsl.edu/cwilj/vol54/iss2/3.
Search in Google Scholar Back to article
Global Artificial Intelligence (AI). Governance, trust, and ethics for sustainable health (GATES): A protocol for methodological framework. ResearchGate. Published 2025. Accessed September 25, 2025. https://www.researchgate.net/publication/394666788_Global_Artificial_Intelligence_AI_Governance_Trust_and_Ethics_for_Sustainable_Health_GATES_A_Protocol_for_Methodological_Framework.
Search in Google Scholar Back to article
Wagner JK, Doerr M, Schmit CD. AI governance: A challenge for public health. JMIR Public Health Surveill. 2024;10(1):e58358. doi:10.2196/58358.
Open DOI Search in Google Scholar Back to article
Yu L, Zhai X. Use of artificial intelligence to address health disparities in low‑ and middle‑income countries: A thematic analysis of ethical issues. Public Health. 2024;234:77–83. doi:10.1016/j.puhe.2024.05.029.
Open DOI Search in Google Scholar Back to article
Johns Hopkins Bloomberg School of Public Health. Bridging the digital divide in health care: A new framework for equity. Published 2025. Accessed September 25, 2025. https://publichealth.jhu.edu/2025/bridging-the-digital-divide-in-health-care-a-new-framework-for-equity.
Search in Google Scholar Back to article
Rincón EHH, Jimenez D, Aguilar LAC, Flórez JMP, Tapia ÁER, Peñuela CLJ. Mapping the use of artificial intelligence in medical education: A scoping review. BMC Med Educ. 2025;25(1):526. doi:10.1186/s12909-025-07089-8.
Open DOI Search in Google Scholar Back to article
Nagi F, Salih RM, Alzubaidi M, et al. Applications of artificial intelligence (AI) in medical education: A scoping review. Published online 2023. doi:10.3233/shti230581.
Open DOI Search in Google Scholar Back to article
Civaner M, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: A cross‑sectional needs assessment. BMC Med Educ. 2022;22(1):772. doi:10.1186/s12909-022-03852-3.
Open DOI Search in Google Scholar Back to article
Weidener L, Fischer M. Proposing a principle‑based approach for teaching AI ethics in medical education. JMIR Med Educ. 2024;10:e55368. doi:10.2196/55368.
Open DOI Search in Google Scholar Back to article
Wang X, He X, Wei J, Liu J, Li Y, Liu X. Application of artificial intelligence to the public health education. Front Public Health. 2023;10:1087174. https://pmc.ncbi.nlm.nih.gov/articles/PMC9872201/.
Search in Google Scholar Back to article
Yang J, Dung NT, Thach PN, et al. Generalizability assessment of AI models across hospitals in a low‑middle and high income country. Nat Commun. 2024;15(1):8270. doi:10.1038/s41467-024-52618-6.
Open DOI Search in Google Scholar Back to article
Maleki Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: Transforming healthcare in the 21st century. Bioengineering (Basel). 2024;11(4):337. https://pmc.ncbi.nlm.nih.gov/articles/PMC11047988/.
Search in Google Scholar Back to article
Ciecierski‑Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare systems in low‑ and middle‑income countries: A systematic scoping review. NPJ Digit Med. 2022;5:162. doi:10.1038/s41746-022-00700-y.
Open DOI Search in Google Scholar Back to article
Obermeyer Z, Abujaber S, Makar M, et al. Emergency care in 59 low‑ and middle‑income countries: A systematic review. Bull World Health Organ. 2015;93(8):577–586. doi:10.2471/BLT.14.148338.
Open DOI Search in Google Scholar Back to article
Stogiannos N, Malik R, Kumar A, et al. Black box no more: A scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK. Br J Radiol. 2023;96(1152):20221157. doi:10.1259/bjr.20221157.
Open DOI Search in Google Scholar Back to article
Reyna MA, Nsoesie EO, Clifford GD. Rethinking algorithm performance metrics for artificial intelligence in diagnostic medicine. JAMA. 2022;328(4):329–330. doi:10.1001/jama.2022.10561.
Open DOI Search in Google Scholar Back to article
López DM, Rico‑Olarte C, Blobel B, Hullin C. Challenges and solutions for transforming health ecosystems in low‑ and middle‑income countries through artificial intelligence. Front Med. 2022;9:958097. doi:10.3389/fmed.2022.958097.
Open DOI Search in Google Scholar Back to article
Gebrewold Y, Andom AT, Ndayizigiye M, Yarbrough C. Bridging the AI gap in clinical imaging: Opportunities and strategies for low‑ and middle‑income countries. J Glob Radiol. 2025;11(2):1–6. doi:10.7191/jgr.985.
Open DOI Search in Google Scholar Back to article
Ramos E, Piló Palax I, Serech Cuxil E, et al. Mobil monitoring Doppler ultrasound (MoMDUS) study: Protocol for a prospective, observational study investigating the use of artificial intelligence and low‑cost Doppler ultrasound for the automated quantification of hypertension, pre‑eclampsia and fetal growth restriction in rural Guatemala. BMJ Open. 2024;14(9):e090503. doi:10.1136/bmjopen-2024-090503.
Open DOI Search in Google Scholar Back to article
Palaniappan K, Lin EYT, Vogel S. Global regulatory frameworks for the use of artificial intelligence (AI) in the healthcare services sector. Healthcare. 2024;12(5):562. doi:10.3390/healthcare12050562.
Open DOI Search in Google Scholar Back to article
Saeed SA, Masters RM. Disparities in health care and the digital divide. Curr Psychiatry Rep. 2021;23(9):61. doi:10.1007/s11920-021-01274-4.
Open DOI Search in Google Scholar Back to article
Osonuga A, Osonuga AA, Fidelis SC, Osonuga GC, Juckes J, Olawade DB. Bridging the digital divide: Artificial intelligence as a catalyst for health equity in primary care settings. Int J Med Inf. 2025;204:106051. doi:10.1016/j.ijmedinf.2025.106051.
Open DOI Search in Google Scholar Back to article
Bradley. Global AI governance: Five key frameworks explained. Bradley. Published 2025. Accessed September 25, 2025. https://www.bradley.com/insights/publications/2025/08/global-ai-governance-five-key-frameworks-explained.
Search in Google Scholar Back to article