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Cited by 4Global Health and SurgeryArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Artificial intelligence for research capacity strengthening: Two reviews and a pathway to shift power in global health

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Wahl, Brian, Nassiri-Ansari, Tiffany, Redpath, Daniel D., Allotey, Pascale, Schwalbe, Nina·2026·PLOS Digital Health
DOI 10.1371/journal.pdig.0001302PMC PMC13086336
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Abstract

Significant disparities persist in how researchers from low- and middle-income countries (LMICs) and high-income countries (HICs) participate in agenda-setting and knowledge production. Rapid advancement in artificial intelligence (AI) might contribute to improving research capacity in LMICs. This review aimed to synthesize evidence on AI for research capacity strengthening in LMICs towards shifting power in global health. We conducted a systematic review of current evidence on AI for research capacity strengthening and a review of reviews on the decolonization of knowledge generation, searching PubMed, Scopus, and SciELO for relevant literature. Articles were included in the systematic review if they included primary data on using AI for research purposes. Reviews were included in the review of reviews if they addressed issues related to knowledge generation. Each review was assigned two independent reviewers for title and abstract screening, full-text review, and data extraction. A narrative synthesis of the extracted data from both reviews was then performed. Given study designs for the inclusion-eligible papers, we did not conduct a formal risk-of-bias assessment. The systematic review identified 305 papers, of which 8 met the inclusion criteria. The review of reviews identified 14 papers, of which 8 were included in the final analysis. Key themes identified from the systematic review include data analysis and research productivity, literature reviews and knowledge management, training and capacity strengthening, expanding access to methodological support, and writing support. The review of reviews found a recurrent theme in the need to address power imbalances rooted in colonial legacies. These reviews demonstrate the potential for AI to transform research capacity in LMICs by democratizing access to advanced analytical tools, providing methodological support, and helping overcome resource limitations that have historically restricted research opportunities. However, equitable governance and local leadership are crucial to prevent AI from widening the gap between LMICs and HICs, perpetuating the power asymmetries that current efforts seek to dismantle.

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  • Digital health sustainability literature emphasizes financing barriers, fragmented markets, and the need for strategic partnerships.

    “Digital health sustainability literature emphasizes financing barriers, fragmented markets, and the need for strategic partnerships [1,5,20,21].”

    From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [20]

    The Results (Review of reviews; page not provided) emphasize sustainable funding models and equitable partnerships, supporting the financing and partnership themes in the context of AI for global health research capacity. The paper does not address fragmented markets, and its focus is narrower than digital health sustainability generally. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Wahl, B., Nassiri-Ansari, T., Redpath, D. D., Allotey, P., & Schwalbe, N.
Affiliations
  • Yale University (US)
  • Queen Margaret University (GB)
  • World Health Organization (CH)
  • Georgetown University (US)
Corresponding author
Wahl, Brian
ORCID
Venue
PLOS Digital Health
Publisher
Public Library of Science (PLoS)
Published
2026-04-16
Volume
5
Issue
4
Pages
e0001302
ISSN
2767-3170 (ISSN-L)
DOI
10.1371/journal.pdig.0001302
PMC
PMC13086336
OpenAlex
W7154590370
Cited by
4
References
5
Citations by year
2026: 4
FWCI
25.72
Type
journal article
Language
en
Open access
Licence
Locations
  • PLOS Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Queen Margaret University Publications Repository (Queen Margaret University) (submittedVersion, cc-by)
Funders
Bill and Melinda Gates Foundation
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Global Health and Surgery
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence, research capacity building, low- and middle-income countries, global health, decolonization of knowledge, power imbalance, colonial legacy, research productivity, AI-assisted literature review, AI-assisted writing, research training, knowledge production

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