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

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

arXiv:2610.35294v1cs.AIcs.CY

TL;DR

This review addresses the poorly documented application of AI in African climate-health settings despite the continent’s disproportionate burden. It compares Scopus-indexed global and African literature using Synthetic Knowledge Synthesis, finding divergent research focuses alongside infrastructure constraints on advanced AI deployment.

  • Problem

    How AI is being adapted or researched in African climate-health contexts remains poorly documented despite Africa’s disproportionate climate-sensitive health burden.

    Source: BACKGROUND · p. 3

  • Method

    The study uses Synthetic Knowledge Synthesis to compare global and African Scopus-indexed literature through bibliometric mapping and qualitative content analysis.

    Source: METHODOLOGY · p. 4

  • Results

    Global research emphasizes complex predictive planetary-health applications, while African research focuses more narrowly on localized infectious-disease applications, amid compute, data-harmonization, and energy constraints.

    Source: Discussion of important differences in… · pp. 9–10, CONCLUSION · p. 10

  • Takeaways & Limitations

    The authors call for investment in infrastructure and localized datasets, and for adapting sophisticated global AI models to African data and infrastructure conditions.

    Source: CONCLUSION · p. 10

    Reliance on English-language publications indexed in major databases may omit grassroots AI implementations documented in regional or non-English repositories.

    Source: Strengths and weaknesses of the study · p. 9

Abstract

from arXiv · show

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.

BACKGROUND

Climate-sensitive health burdens are acute in Africa, while the fit and reach of AI applications there remain poorly documented. The review compares global and African research and identifies divergent priorities, structural barriers, and possible applications.

BACKGROUND

  • Limited compute infrastructure, fragmented data, and energy instability constrain translation of advanced AI into African public-health frameworks.The discussion characterizes these as structural deficits rather than a lack of conceptual understanding.Source: BACKGROUND · p. 4

METHODOLOGY

  • Synthetic Knowledge Synthesis compares Scopus-indexed global and African corpora using bibliometric mapping and qualitative content analysis.The method analyzes retrieved corpora computationally rather than screening and excluding individual articles through traditional PRISMA procedures.Source: METHODOLOGY · p. 4

Statement of principal findings

  • Global research emphasizes complex predictive planetary-health applications, while African research centers on localized, reactive infectious-disease triage and narrower disease-focused uses.The review also identifies compute deficits, fragmented data, and energy instability as constraints on advanced AI deployment in Africa.Source: Statement of principal findings · p. 6

Sea Temperature and Oceanography

  • AI applications proposed for oceans and climate include coastal-hazard warnings, fisheries monitoring, and higher-resolution local climate models using sparse weather-station data.The review also describes renewable-grid optimization, EV-charging-site selection, and emissions monitoring as applications for energy and transport.Source: Sea Temperature and Oceanography · p. 6

Carbon Emission Prediction and Electric Vehicles (EVs)

  • Renewable-grid forecasting and AI-based EV-charging placement are presented as ways to support grid stability and sustainable mobility in African cities.The review also describes real-time industrial and transport emissions monitoring for policy and reduction targets.Source: Carbon Emission Prediction and… · pp. 6–7

Health and Socio-Economic Impact

  • Health applications span AI-assisted diagnosis, outbreak forecasting, exposome-based risk assessment, and localized warnings for floods and droughts.The proposed tools include computer vision for X-rays and blood smears, disease models combining climate and mobility data, and satellite, radar, and social-media inputs for hazard forecasts.Source: Health and Socio-Economic Impact · p. 7, Digitalization and Disaster Response · p. 8

Digital Health Innovation

  • Digital-health proposals include acoustic leak detection linked to cholera prevention, solar microgrid management for clinic power, and interoperable health data systems.The review cautions that country readiness varies, so deployment recommendations should account for local infrastructure conditions.Source: Digital Health Innovation · pp. 8–9

Strengths and weaknesses of the study

  • The review is limited to English-language publications indexed in major databases, potentially omitting grassroots AI implementations documented in regional or non-English repositories.Source: Strengths and weaknesses of the study · p. 9

Strengths and weaknesses in relation to other studies

The study distinguishes itself by examining implementation realities and isolating African research rather than treating global research as uniform.

Strengths and weaknesses in relation to other studies

Discussion of important differences in results

The discussion contrasts global predictive environmental modeling with African reactive applications and attributes the adoption gap to structural deficits. The conclusion describes a narrower African research focus and calls for contextualized investment and infrastructure.

Discussion of important differences in results

CONCLUSION

  • Global AI research spans agriculture, air pollution, sustainability, and non-communicable diseases, whereas African studies focus more narrowly on supervised machine learning for specific diseases and basic sustainability.Examples of African disease applications include HIV and malaria.Source: CONCLUSION · p. 10
  • Energy instability, limited compute access, and data fragmentation constrain advanced AI use; the authors identify targeted finance, including Green Bonds and Public-Private Partnerships, as a route to de-risking investment.Source: CONCLUSION · p. 10
  • The authors call for international and national funding to adapt advanced global AI models to African data and infrastructure challenges.They also recommend multidisciplinary teams to ensure applications fit local climate-health vulnerabilities.Source: CONCLUSION · p. 10
  • The compute deficit includes scarce localized data centers and specialized hardware; energy instability and costly connectivity sustain a usage gap that limits indigenous innovation scalability.The conclusion presents investment in sovereign compute and localized datasets as a prerequisite for economic agency.Source: CONCLUSION · p. 10
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