EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Oct 31, 2026 25 10

AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa

Adeyemi Sallam Febilola 1,
Muhammed Adewale Alimi 1,
Isaac Tobechukwu Onah 1,
Chikeluba Joseph Emeonye 1,
Ahmad Onikoko Abdulgafar 2 * ,
Nyimeawiya Ngema Nyee 3,
Ibrahim Adekunle Babawale 4
1 Faculty of Pharmaceutical Sciences, Usmanu Danfodiyo University Sokoto, Sokoto State, Nigeria2 School of Medical Laboratory Science, Usmanu Danfodiyo University, Sokoto (UDUS), Nigeria3 Nursing, Lublin Academia WSEI, Warsaw, Poland4 Department of Anatomy, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria* Corresponding Author
Epidemiology and Health Data Insights, 2(5), 2026, ehdi063, https://doi.org/10.63946/ehdi/19706
Publication date: Oct 31, 2026
Full Text (PDF)

ABSTRACT

Background: Generative AI and large language models are changing how people seek health information, but their effect on maternal health in sub-Saharan Africa is unclear. This narrative review synthesizes evidence on generative AI and maternal health misinformation, examines community health workers (CHWs) as information intermediaries, and proposes a conceptual framework for safe AI implementation.
Methods: We ran a purposive search of PubMed/MEDLINE, Web of Science, Scopus, ACM Digital Library, and Google Scholar for publications from January 2014 to August 2026. We synthesized findings narratively and used the Consolidated Framework for Implementation Research (CFIR) to organize implementation determinants.
Results: We found no study of generative AI maternal health misinformation in sub-Saharan Africa. Evidence from high-income settings and non-maternal topics shows AI answers on reproductive health can be inaccurate or inconsistent, and fluent AI text can seem more compelling than human writing. Multi-specialty African benchmarks and a global benchmark review point to a gap between benchmark performance and clinical safety, but maternal-specific performance is unreported. CHWs have a well-documented record of improving maternal and child health, but their ability to identify AI errors is untested. African digital health implementations highlight user-centered design, interoperability, and sustained investment.
Conclusions: We propose, as hypotheses for evaluation, localized benchmarking, retrieval-augmented generation restricted to national guidelines with added safeguards, CHW curricula that include AI literacy, and decentralized research hubs. Unvalidated AI could bypass CHW authority and affect trust, though this is unstudied. Priorities include measuring AI use among pregnant women and prospectively evaluating CHW-facing tools.

KEYWORDS

Generative AI Maternal Health Misinformation Community Health Workers Sub-Saharan Africa Implementation Science

CITATION (Vancouver)

Febilola AS, Alimi MA, Onah IT, Emeonye CJ, Abdulgafar AO, Nyee NN, et al. AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa. Epidemiology and Health Data Insights. 2026;2(5):ehdi063. https://doi.org/10.63946/ehdi/19706
APA
Febilola, A. S., Alimi, M. A., Onah, I. T., Emeonye, C. J., Abdulgafar, A. O., Nyee, N. N., & Babawale, I. A. (2026). AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa. Epidemiology and Health Data Insights, 2(5), ehdi063. https://doi.org/10.63946/ehdi/19706
Harvard
Febilola, A. S., Alimi, M. A., Onah, I. T., Emeonye, C. J., Abdulgafar, A. O., Nyee, N. N., and Babawale, I. A. (2026). AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa. Epidemiology and Health Data Insights, 2(5), ehdi063. https://doi.org/10.63946/ehdi/19706
AMA
Febilola AS, Alimi MA, Onah IT, et al. AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa. Epidemiology and Health Data Insights. 2026;2(5), ehdi063. https://doi.org/10.63946/ehdi/19706
Chicago
Febilola, Adeyemi Sallam, Muhammed Adewale Alimi, Isaac Tobechukwu Onah, Chikeluba Joseph Emeonye, Ahmad Onikoko Abdulgafar, Nyimeawiya Ngema Nyee, and Ibrahim Adekunle Babawale. "AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa". Epidemiology and Health Data Insights 2026 2 no. 5 (2026): ehdi063. https://doi.org/10.63946/ehdi/19706
MLA
Febilola, Adeyemi Sallam et al. "AI, Misinformation, and the Maternal Health Information Ecosystem: A Narrative Review and Implementation Framework for Sub-Saharan Africa". Epidemiology and Health Data Insights, vol. 2, no. 5, 2026, ehdi063. https://doi.org/10.63946/ehdi/19706

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