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

Brian Wahl, Tiffany Nassiri-Ansari, Daniel D Redpath, Pascale Allotey and Nina Schwalbe

PLOS Digital Health, 2026, vol. 5, issue 4, 1-14

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.Author summary: Global health research has been shaped by inequalities rooted in colonial legacies. Researchers in wealthier countries have, in general, led the establishment of research priorities and knowledge generation. Artificial intelligence tools are rapidly changing how research is done. We wanted to understand whether these technologies could help shift the power balance in global health by strengthening the ability of researchers in lower-resourced settings to lead research efforts. We conducted two complementary literature reviews: one examining how artificial intelligence is being used to build research skills and capacity, and another exploring efforts to decolonize knowledge production in global health. We found that artificial intelligence tools show promise in areas like data analysis, literature management, and scientific writing, potentially helping researchers overcome barriers related to limited infrastructure or expertise. However, the evidence base remains limited, and there are real risks that these technologies could deepen existing inequalities if developed without meaningful input from the communities they aim to serve. Our findings suggest that realizing the benefits of artificial intelligence for global health research depends on centering local leadership, equitable governance, and sustained investment in people and systems.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001302

DOI: 10.1371/journal.pdig.0001302

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