SynthesisFrontiers in artificial intelligence2026
The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Sub-Saharan Africa continues to face a substantial burden of infectious diseases, many of which are zoonotic and shaped by complex interactions across human, animal, and environmental systems. Artificial Intelligence (AI), encompassing machine learning (ML) and deep-learning (DL) techniques, has emerged as a powerful tool for enhancing disease prediction, surveillance, diagnosis, and decision-making within a One Health (OH) framework. Method: This systematic review synthesizes evidence from 62 peer-reviewed studies to assess how AI-based modelling techniques have been applied to infectious disease research across Sub-Saharan Africa. Results: Results show that AI adoption has grown rapidly since 2019, with a pronounced surge in publications between 2021 and 2024. However, research leadership and implementation capacity remain geographically uneven, with South Africa, Ethiopia, Kenya, and Tanzania dominating the landscape. Across studies, AI tools were used primarily for classification and prediction tasks, with ensemble models and deep-learning architectures showing the strongest performance (with median accuracy close to 100% for Convolutional Neural Network model). Malaria (24%), HIV (12%), COVID-19 (12%), and Tuberculosis (6.7%) were the most frequently targeted diseases, while zoonotic and environmentally linked infections were comparatively underrepresented. Most studies relied exclusively on human data, revealing a persistent gap in the integration of animal and environmental components critical to the OH paradigm. Conclusion: Despite promising applications, including image-based parasite detection, IoT-enabled surveillance, ecological risk modelling, and smartphone-assisted diagnostics, AI deployment remains constrained by limited computational infrastructure, inadequate digital connectivity, data-governance weaknesses, and shortages of AI-trained specialists. Conversely, expanding mobile connectivity, cloud-based analytics, and advancements in multilingual AI tools could create new opportunities to strengthen surveillance systems, empower health workers, and improve community engagement.
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