Artificial Intelligence in Medical Education in Nigeria: Current Applications, Challenges, and Future Directions

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.