AI use in Africa faces barriers for climate health solutions

Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa

Artificial IntelligenceComputers and Society

Summary

Climate change hurts health in Africa a lot, but using AI to predict and manage these problems is still very limited. The authors looked at published research and found that global studies focus on big, complex AI models for overall planetary health. In contrast, African studies mostly work on small, local solutions for immediate health issues like infectious diseases. Africa's challenges like poor computing resources, fragmented data, and unreliable energy make it hard to develop advanced AI systems. The authors suggest that funding should move from small projects to building strong digital infrastructure and AI tools designed for local needs.

What this means in practice

  • For public health policymakers: Design funding strategies that prioritize building digital infrastructure and local AI capabilities for managing climate-related health issues in Africa.
  • For global health ngos: Develop localized AI tools for infectious disease triage in African regions where advanced computing resources are limited.

A survey. It maps existing work.

Authors

Peter Kokol, Martha Kachweka, Helena Blazun Vosner, Samuel Chol Buol, Jernej Zavrsnik, Bojan Zlahtic, Grega Zlahtic

Abstract

While climate change disproportionately impacts African public health, AI predictive modeling remains heavily constrained. Using a Synthetic Knowledge Synthesis approach and VOSviewer, we conducted a bibliometric analysis of Scopus-indexed literature, comparing Global and African corpora. Network analysis reveals stark divergences: global research emphasizes complex, predictive models for planetary health, whereas African research focuses on localized, reactive solutions for immediate infectious disease triage. Severe structural barriers in Africa, including compute deficits, data silos, and energy instability, hinder advanced AI scaling. Ultimately, international funding must shift from siloed pilot projects toward building foundational digital infrastructure and localized AI frameworks.
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