ReviewWellcome open research2025
Community engagement for artificial intelligence health research in Africa.
Review in Wellcome open research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Advancing epidemic intelligence: evaluating Senegal's mpox surveillance system and readiness for AI-driven predictive modelling.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence holds the potential to benefit communities in numerous areas, including health. Artificial intelligence health research is, among other things, advancing the accuracy of diagnosis, enabling new drug and treatment options, and reducing costs in healthcare. Like elsewhere, artificial intelligence health research is rapidly expanding across the African continent; however, numerous co-travelling ethical challenges - including those related to data protection, equitable access, and data colonization - are under-addressed. Community engagement is a process through which a number of pertinent health research ethics issues affecting communities can be identified and collaboratively pursued; however, there currently is limited understanding of the opportunities and challenges for community engagement in artificial intelligence research globally. In order to advance collective understanding and support policy and practice innovation, this paper interrogates how communities in Africa could be engaged in artificial intelligence health research. It provides a justification for community engagement in artificial intelligence health research, and discusses its application in African communities. It concludes by offering some context-specific recommendations for priority attention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.