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This bibliometric study maps how AI is used in climate-related health research, contrasting global work on predictive planetary-health models with African work on reactive infectious-disease triage. Its most useful finding for AI readers is the structural gap: compute shortages, data silos, and unstable energy limit African AI deployment, which the authors argue calls for funding foundational digital infrastructure over isolated pilots. The paper is a literature analysis rather than a demonstrated system, so its claims rest on Scopus-indexed publication patterns.
Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa
arXiv:2610.35294v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
arXiv:2610.35294 [cs.AI]
(or arXiv:2610.35294v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.35294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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