Artificial Intelligence in Medical Education in Nigeria: Current Applications, Challenges, and Future Directions
DOI:
https://doi.org/10.66811/eijrihs.vol1.no3.44Keywords:
Artificial intelligence; Medical education; Nigeria; Brain drain; Digital health; OphthalmologyAbstract
Abstract
Background: Nigeria's medical education system operates under compounding structural pressure, marked by overcrowded institutions, declining training standards, and a persistent loss of skilled doctors to international migration. Artificial intelligence (AI) has emerged globally as a transformative pedagogical tool, yet its application within Nigerian medical education remains poorly characterised. This narrative review examines current AI applications in medical education, identifies barriers to adoption within the Nigerian context, and proposes AI integration as a retention-oriented intervention in the country's healthcare workforce crisis, with particular attention to ophthalmology training.
Methods: A narrative review of peer-reviewed literature was conducted across PubMed, MEDLINE, Scopus, and Google Scholar covering publications from 2019 to 2026, supplemented by Nigerian institutional reports and policy documents. Search terms included combinations of artificial intelligence, medical education, Nigeria, digital health, and brain drain. Findings were synthesised thematically.
Results: AI applications in medical education globally include adaptive learning platforms, automated assessment generation, and clinical simulation. In Nigeria, a majority of surveyed medical and allied health students report informal use of generative AI tools, driven by efficiency rather than institutional guidance. Adoption barriers include limited broadband infrastructure, low digital literacy among educators, absent regulatory frameworks, and inadequate simulation resources. Within ophthalmology, AI-generated virtual patient scenarios offer a reproducible alternative to scarce physical simulation equipment.
Conclusion: AI integration in Nigerian medical education should be reframed as a structural workforce intervention rather than a pedagogical novelty. Coordinated investment in broadband infrastructure, context-specific AI model development, formal curricular integration, and national regulatory frameworks offers a tractable pathway toward improved training quality and physician retention.
Downloads
References
Adeyanju, O., et al. (2025). Unexplored tool for sustainable development: Can artificial intelligence promote good health and well-being in Africa? *Third World Quarterly. https://doi.org/10.1080/00358533.2025.2561726
Bin Abdulrahman, K. A., et al. (2026). Theoretical evolution of AI in medical education: Models, frameworks, and future directions. Frontiers in Education, 11, 1796632. https://doi.org/10.3389/feduc.2026.1796632
David-Olawade, A. C., Wada, O. Z., Adeniji, Y. J., Aderupoko, I. V., & Olawade, D. B. (2025). Artificial intelligence readiness among healthcare students in Nigeria: A cross-sectional study assessing knowledge gaps, exposure, and adoption willingness. International Journal of Medical Informatics, 204, 106085. https://doi.org/10.1016/j.ijmedinf.2025.106085
Hallquist, E., Gupta, I., Montalbano, M., & Loukas, M. (2025). Applications of artificial intelligence in medical education: A systematic review. Cureus, 17(3), e79878. https://doi.org/10.7759/cureus.79878
Internet access in Nigeria: A comprehensive overview. (2026, January 3). TS2 Tech. Retrieved June 22, 2026, from https://ts2.tech/en/internet-access-in-nigeria-a-comprehensive-overview/
Mateen, M. A., Kumar, V., Singh, A., Yadav, P., Mahto, R., Hassan, S., et al. (2025). Impact of generative AI in medical education in India: A systematic review. Frontiers in Artificial Intelligence, 8, 1704785. https://doi.org/10.3389/frai.2025.1704785
Mohac Africa. (2026, February 22). Technology in African healthcare: 2026 digital health innovations. TechCabal. Retrieved June 22, 2026, from https://mohacafrica.org/technology-in-african-healthcare/
Nigerian Communications Commission. (2025, December 24). Nigeria's internet penetration reaches 50% as 70% target falls short. TechCabal. Retrieved June 22, 2026, from https://techcabal.com/2025/12/24/nigeria-50-percent-internet-penetration-broadband-target/
Ogbonna, O., Ezeabasili, A. C. C., & Ifeoma, J. N. (2026). Artificial intelligence and health education in Nigeria. SVOA Medical Research, 4(2), 67-81. https://doi.org/10.58624/SVOAMR.2026.04.009
Pupic, N., Ghaffari-zadeh, A., Hu, R., Singla, R., Darras, K., & Karwowska, A., et al. (2023). An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0000255
Real-time AI-driven pipeline for automated medical study content generation in low-resource settings: A Kenyan case study. (2025). arXiv. Retrieved June 22, 2026, from https://arxiv.org/pdf/2507.05212
Shaw, K., Henning, M. A., & Webster, C. S. (2025). Artificial intelligence in medical education: A scoping review of the evidence for efficacy and future directions. Medical Science Educator. https://doi.org/10.1007/s40670-025-02373-0
Toluhi, J., Ilori, T., & Akpa, O. (2023). Medical education in Nigeria and migration: A mixed methods study of how the perception of quality influences migration decision-making. *BMC Medical Education, 23,* 891. https://doi.org/10.1186/s12909-023-04920-y
Umar, A. A., Salihu, H. M., & Azuine, R. E. (2025). Crisis of brain drain in Nigeria's health sector: Challenges, opportunities, and the path forward. International Journal of MCH and AIDS, 14, e011. https://doi.org/10.25259/IJMA_11_2025
