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

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.