Promise to Practice: Reimagining Artificial Intelligence for Equitable Global Health Impact
Year: 2026
Type: article
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.
Source: Annals of Global Health
DOI: 10.5334/aogh.5268
Institutions: Brown University, Africa Health Sciences University, Georgetown University, Duke University, Kwame Nkrumah University of Science and Technology, University of California, Davis, University of Ibadan, Durban University of Technology, Johns Hopkins University, Johns Hopkins Medicine, Emory University, Georgia Institute of Technology, Veterans Health Administration, Atlanta VA Medical Center, Fatima Jinnah Medical University, Stanford Medicine, Stanford University, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Consortium of Universities for Global Health
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7163390140