Artificial Intelligence and Health Education in Nigeria: Implications for Healthcare Delivery, Workforce Capacity, and Digital Health Systems

Artificial Intelligence (AI) is slowly reconstituting health systems globally with new approaches to diagnosis, disease surveillance, clinical decision-making and service delivery. In Nigeria, where health infrastructure faces persistent challenges-verging on limited funding, workforce shortages, uneven access to care and fragmented data systems. AI is also a test of preparedness and an opportunity. We investigate the implications, uses and health informatics problems of AI adoption within the Nigerian health system. The paper discusses several practical applications including but not limited to AI-assisted radiology, predictive modeling for infectious disease outbreaks, telemedicine support tools, EMR optimization and community level decision support. Such technologies have the potential to enhance early detection of high-burden diseases (including tuberculosis, malaria, cancer, and complications associated with pregnancy) while also democratizing access among disadvantaged rural populations. However, the success of such implementation heavily relies on health data quality, availability and interoperability. Key barriers remain significant. Many health facilities still use paper-based records, digital infrastructure is uneven, electricity and internet access are unreliable in some areas, and capacity for health informatics is sparse. Adoption of these technologies is further complicated by ethical considerations such as data privacy, algorithmic bias, accountability and patient trust. Emerging regulatory frameworks are weak, coordination and enforcement capacity for governance need strengthening. The paper concludes that AI can play a meaningful role in supporting Nigeria’s health system if deployed responsibly with strong data systems and sound workforce training, and guided by clear ethical and regulatory safeguards. What made sense for AI-assisted generated health solutions is the principle of ensuring that they do not replace human expertise while keeping patientcentric and equitable health eValue- outcomes.