Harnessing Artificial Intelligence for Public Health Surveillance in Africa: Current Applications, Challenges, and Opportunities: A Scoping Review

John Etim Edet, Adamu Ishaka Akyala, Agbo Omudu Edward, Anthony Asukwo Ikaiddi, Jake Epelle, Etieno Etimedet, Islamiyyat Adekemi Olatinwo, Chinwe Grace Azuka, Michael Idoko Abah, Philip Asuquo Edem and Oluwakemi Oluwagbohun
Additional contact information
John Etim Edet: Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nasarawa State, Nigeria.
Adamu Ishaka Akyala: Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nasarawa State, Nigeria.
Agbo Omudu Edward: Department of Biological Science, Benue State University, Makurdi, Benue State, Nigeria
Anthony Asukwo Ikaiddi: Department of Chemistry, Georgia State University, USA
Jake Epelle: TAF Africa (NGO).
Etieno Etimedet: Department of Nursing Science, Amadu Bello University, Zaria, Nigeria
Islamiyyat Adekemi Olatinwo: Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nasarawa State, Nigeria.
Chinwe Grace Azuka: World Health Organization
Michael Idoko Abah: Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nasarawa State, Nigeria.
Philip Asuquo Edem: Department of Geology, University of Calabar, Calabar, Niger
Oluwakemi Oluwagbohun: Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nasarawa State, Nigeria.

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 7, 2791-2828

Abstract: Background: Artificial Intelligence (AI) is increasingly revolutionizing public health surveillance, particularly in regions with constrained healthcare infrastructure. This scoping review examines the application of AI in public health surveillance across Africa, identifying existing implementations, challenges, and opportunities. AI technologies such as machine learning, natural language processing, and predictive analytics enhance epidemic intelligence by analyzing vast datasets from diverse sources, including electronic health records, social media, and environmental sensors. These AI-driven tools provide early warnings for outbreaks, improve disease surveillance, and facilitate timely public health responses. Methods: A systematic search of databases, including Pubmed, Google Scholar, Researchgate, Web of Science, Scopus, Scientific Research, African Journal of Health Informatics, International Journal of Infectious Diseases, ScienceDirect, African Journal of Biotechnology, PloS One, The Lancet, JMIMR, BMJ, and BMC. The search covers publications from January 2010 to February 2025, spanning for 15 years. A total of 1411 articles. An additional 44 records were identified through other sources after removing 201 duplicates; 1254 unique articles were screened based on titles and abstracts. One thousand one hundred and twenty-seven (1127) records were excluded as they did not meet the inclusion criteria. Then, 127 full-text articles were assessed for eligibility, and 54 full-text articles were excluded for various reasons: study in non-African location (52) Not focused on AI applications (12), challenges and opportunities, Insufficient Data (9) Finally, 54 studies were included in the qualitative synthesis. Results: Following a rigorous selection process, 54 studies were included in the qualitative synthesis. Most studies (83.33%) were published as peer-reviewed journal articles, while technical reports and theses were less common, with five (9.26%) and four (7.41%) studies, respectively. The primary focus of these studies varied: 39 (72.22%) explored AI applications in disease detection and prediction, 25 (46.30%) examined AI applications in disease surveillance, 18 (33.33%) highlighted challenges in AI adoption for healthcare, and 15 (27.78%) focused on real-time surveillance and reporting in Africa. Findings reveal that AI is actively utilized in African public health systems for disease prediction, outbreak surveillance, and resource allocation. However, several challenges hinder its full potential, including inadequate infrastructure, data privacy concerns, limited access to high-quality datasets, and a shortage of AI-trained healthcare professionals. Despite these barriers, AI presents great opportunities for strengthening health security in Africa by improving diagnostic accuracy, optimizing healthcare interventions, and enhancing real-time epidemiological analysis. Conclusion: Artificial intelligence presents a transformative opportunity for health surveillance in Africa, particularly in diagnostics and disease prediction. AI-powered tools, such as mobile diagnostic applications and predictive models, enhance healthcare accessibility in resource-limited settings by analyzing vast datasets for early disease detection. Successful implementations, including AI-driven malaria mapping and tuberculosis detection through chest X-ray analysis, HIV, cholera, Ebola, measles, Zika virus, and malaria, enabling targeted screening interventions, personalized treatment plans, and efficient resource allocation, demonstrate AI’s potential to improve public health outcomes. Despite challenges such as infrastructure limitations and data privacy concerns, AI continues to revolutionize disease monitoring and response. By leveraging machine learning for targeted interventions and efficient resource allocation, AI holds promise for a future of more proactive and effective healthcare across the continent of Africa.

Date: 2025
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