Artificial intelligence (AI) is already reshaping healthcare across Africa, from diagnostics and clinical decision support, to surveillance and supply chain optimization. Yet a significant share of AI’s promised value remains unrealized because inclusion is not intentionally built in at each level of the system. Drawing on original analysis, this paper shows that ~32% of AI’s potential value in African healthcare depends on correcting bias and exclusion across the full AI stack. In other words, nearly one-third of Africa’s healthcare AI dividend will remain unrealized if bias and inequity are not addressed.
This paper introduces a full-stack, equity-by-design framework and lays out concrete design recommendations for innovators, funders, and policymakers seeking to unlock trust, accuracy, and scale in AI for African healthcare. The takeaway is clear. Every layer of the AI stack offers a choice: to either replicate exclusion or create value at scale.
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