Artificial Intelligence in African Healthcare: Catalyzing Innovation While Confronting Structural Challenges

Abstract

Background:Artificial intelligence (AI) has emerged as a transformative force in global health, promising to improve diagnostic accuracy, optimize health systems, and enable real-time epidemiological surveillance. In Africa, where healthcare systems are often under-resourced yet rapidly digitizing, AI represents a dual opportunity: to leapfrog infrastructure limitations and to build context-specific solutions for persistent health inequities.Objective:This review examines the current landscape of AI in African healthcare, highlighting practical applications, emerging innovations, and systemic barriers. It explores how AI is catalyzing innovation across disease surveillance, diagnostics, supply chain management, and telehealth while unpacking the structural, ethical, and governance challenges that may hinder sustainable progress.Methods:A structured narrative review was conducted using peer-reviewed literature, regional policy reports, and case studies from academic and grey sources. The review synthesizes evidence from African countries actively deploying AI in healthcare and identifies common trends, gaps, and opportunities for future scale-up.Findings:AI-enabled interventions in Africa, such as algorithm-based TB screening, drone-assisted vaccine delivery, and chatbot-supported mental health care, demonstrate substantial potential. However, challenges persist around data governance, infrastructural disparities, algorithmic bias, and the lack of local capacity. The risk of digital colonialism remains high unless innovation is driven by African stakeholders and tailored to local contexts.Conclusion:For AI to meaningfully transform healthcare in Africa, investment must be channeled into ethical, inclusive, and African-led innovation ecosystems. Without addressing systemic barriers, the promise of AI may deepen health inequities rather than resolve them.

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