AI evidence extraction

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, Nina Schwalbe · PLOS Digital Health · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

15.51

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1371/journal.pdig.0001302

Methodology & findings

Study design

Umbrella review comprising two components: (1) systematic review of primary studies on AI for research capacity strengthening in LMICs, searching PubMed, Scopus, and SciELO; (2) review of reviews on decolonization of knowledge generation.

Sample

100–500, 4 groups

Primary method

Narrative synthesis of extracted data from both reviews. No formal statistical analysis, meta-analysis, or meta-regression conducted. Dual independent review process for screening and data extraction.

Main result

The systematic review identified 305 papers, of which 8 met the inclusion criteria, while 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 reviews demonstrate that "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."

Reports effect sizes.

Research paradigm

Critical realism with decolonial epistemology

Author conclusions

The authors conclude that "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."

Risk of bias

No formal risk-of-bias assessment conducted; Potential publication bias in included studies; Language bias possible (searches in PubMed, Scopus, SciELO may have language preferences); Selection bias in study inclusion criteria; Heterogeneity of study designs precluded formal meta-analysis; Limited number of inclusion-eligible studies (8 in systematic review, 8 in review of reviews); Potential publication bias not assessed; Dependence on narrative synthesis rather than quantitative pooling; Selection bias potential from narrow inclusion criteria (only 8 of 305 papers met criteria in systematic review); Publication bias not explicitly assessed; Language bias possible from database selection (PubMed, Scopus, SciELO)

Limitations

"Given study designs for the inclusion-eligible papers, we did not conduct a formal risk-of-bias assessment." The authors note that "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," suggesting limitations in the current evidence base regarding implementation of equitable AI use.

Open questions raised

The authors identify a need for implementation research on AI governance frameworks in LMICs, investigation of power dynamics in AI-enabled research capacity building, empirical evidence on effectiveness of AI interventions for capacity strengthening, and strategies to prevent widening of research equity gaps between LMICs and HICs.
The review of reviews identified "a recurrent theme in the need to address power imbalances rooted in colonial legacies," suggesting this is a critical gap requiring future attention. The abstract indicates that equitable governance models and mechanisms for maintaining local leadership in AI implementation for research capacity remain underdeveloped areas.
Need for equitable governance frameworks in AI implementation; requirement for local leadership in LMIC research contexts; need to address power imbalances rooted in colonial legacies; mechanisms to prevent AI from widening research capacity gaps between LMICs and HICs

Data: not_statedCode: not_statedExtracted from: pdfAgreement 62%

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