Investigating the Adoption Barriers of Artificial Intelligence in Nigerian Healthcare Software Systems

Artificial intelligence holds transformative potential for healthcare delivery in Nigeria, yet adoption within clinical software systems remains minimal despite demonstrated global applications. This study investigates the barriers to AI adoption in Nigerian healthcare software systems, drawing empirical data from health technology developers, hospital IT managers, and clinical staff across the South East, North East, and South West zones. A mixed-methods sequential explanatory design is employed, beginning with a survey of 80 healthcare technology stakeholders followed by qualitative interviews with 15 purposively selected experts. Quantitative data is analysed using factor analysis to identify barrier clusters, while thematic analysis extracts explanatory narratives. Four major barrier clusters emerge: data infrastructure deficiency, regulatory ambiguity regarding NAFDAC guidance for AI-based medical software, algorithmic trust deficit among clinicians, and talent scarcity in Nigerian health IT firms. The study critically examines each barrier against solutions implemented in comparable low-resource health systems including Kenya and Ghana. A contextualised AI adoption readiness model for Nigerian healthcare is proposed, mapping prerequisite conditions for each adoption phase. Recommendations address both supply-side engineering capacity building and demand-side clinical AI literacy requirements for sustainable adoption. Keywords: artificial intelligence, healthcare software, adoption barriers, Nigeria, clinical AI