Evidence map›Paper›PMID 42256553›Full record

ReviewAnnals of global health2026

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 and 6 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Annals of global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Shama PatelBrown University, Division of Global Emergency Medicine, Adjunct Faculty, Providence, RI, USA.
Katherine O RobskyCenter for Global Health Practice and Impact, Georgetown University, Washington, DC, USA.
Wenhui MaoDuke Global Health Innovation Center, Duke University, Durham, NC, USA.
Umar UsmanAFREhealth West African Chapter, Secretariat, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Lorena GarciaUniversity of California Davis, School of Medicine, Davis, CA, USA.
Olubukola OmobowaleRehabilitative and Social Medicine Unit, Department of Community Medicine, College of Medicine, University of Ibadan, Nigeria.
Gugu MchunuDurban University of Technology, Faculty of Health Sciences, South Africa.
Joowhan SungJohns Hopkins University School of Medicine, Division of Infectious Diseases, Baltimore, MD, USA.
Gari CliffordEmory University, Department of Biomedical Informatics, Atlanta, GA, USA.
Anant MadabhushiGeorgia Institute of Technology and Emory University, Department of Biomedical Engineering, Atlanta, GA, USA.
Azra IsmailEmory University, Department of Biomedical Informatics, Atlanta, GA, USA.
Saara BidiwalaCenter for Global Health Practice and Impact, Georgetown University, Washington, DC, USA.
Seemab MehmoodFatima Jinnah Medical University, Lahore, Pakistan.
Christine NgaruiyaStanford School of Medicine, Stanford EM International Global and Population Health Section (SEMI), Department of Emergency Medicine, Palo Alto, CA, USA.
Paul E KilgoreWayne State University, Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, and the Center for Emerging and Infectious Diseases, Detroit, Michigan, USA.
Keith MartinConsortium of Universities for Global Health, Washington, DC, USA.

Funding

BLRD VA IK6 BX006185
6 · The paper itself

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.

Indexed as

Artificial IntelligenceGlobal HealthHealth EquityDigital HealthHumansartificial intelligencedigital healthglobal healthhealth equitymachine learningprediction modelstechnology

Identifiers

PMID42256553
PMCPMC13239324

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.