Artificial Intelligence for Public Health in Africa: Why the Real Goal Is Public Value, Not More Pilots

Artificial intelligence is often presented as the next big answer to Africa's health challenges. Across the continent, there is growing excitement about AI tools for disease surveillance, clinical decision support, primary care, and health planning. But a new paper by Adebisi and colleagues in The Lancet Regional Health – Africa makes an important point: Africa does not simply need more AI pilots. It needs AI that creates real public value.

This argument matters. Too often, promising digital health and AI projects are launched with energy and visibility, only to remain small, fragmented, short-lived, or externally driven. The result is a familiar cycle: innovation is celebrated, but health systems do not fundamentally change. According to the paper, the challenge in many African settings is not a lack of ideas. The deeper problem lies in weak foundations, including fragmented data systems, poor interoperability, uneven internet connectivity, unreliable electricity, and limited institutional capacity to govern and scale AI responsibly.

AI as a health-systems intervention

That is why the paper's central message is so strong. AI should not be treated as a flashy technical add-on. It should be approached as a health-systems intervention. In practical terms, this means AI must be embedded within strong governance structures, aligned with national priorities, and designed to support equity rather than deepen existing inequalities. The focus should not be on whether a tool is new or impressive, but on whether it improves public health in ways that are sustainable, accountable, and meaningful for communities.

AI should not be treated as a flashy technical add-on. It should be approached as a health-systems intervention.

A timely intervention for Africa

This is a timely intervention for Africa. Many countries are under pressure to modernise their health systems and to show they are keeping pace with global technological change. Yet technology alone does not solve systemic problems. If AI is introduced into systems that already struggle with weak infrastructure, underfunded institutions, and limited regulatory capacity, it may simply reproduce old problems in new forms. In some cases, it could even widen inequities by privileging urban, connected, and data-rich populations while leaving behind rural and underserved communities. The paper therefore calls for a shift in mindset: from pilot enthusiasm to long-term value creation.

What public value really means

For policymakers, researchers, funders, and innovators, the lesson is clear. The future of AI in African public health should not be measured by the number of projects launched, but by whether these tools strengthen surveillance, improve decision-making, support frontline systems, and deliver fairer health outcomes at scale. Public value means building solutions that last, that can be governed, and that respond to real public health needs.

Beyond pilots, toward purpose

Africa does not need to be a testing ground for disconnected experiments. It needs thoughtful, equitable, and system-ready AI. Moving beyond pilots is not about rejecting innovation. It is about demanding better innovation, rooted in public purpose and built to serve populations, not just headlines. That is the real promise of AI for public health in Africa.

Yusuff Adebayo Adebisi

Pharmacist and epidemiologist advancing equity-driven, policy-relevant public health research. PhD Researcher at the University of Glasgow. Director of Research at Global Health Focus.

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