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, our paper estimates that nearly one-third of the potential value of AI in African healthcare could remain unrealized if bias and exclusion are not addressed across data, models, infrastructure, and governance systems.
This summary provides an overview of the full report introducing a practical “Equity Stack” framework that maps where inclusion must be built into AI systems – from datasets and model development to infrastructure, funding, policy, and institutional capacity. It outlines concrete actions for innovators, funders, and policymakers seeking to unlock both the equity and economic dividends of AI, showing how coordinated design across the stack can improve performance, increase adoption, and ensure AI systems work for the communities they are meant to serve.
