{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T01:21:38Z","timestamp":1792027298919,"version":"4.4.0"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1778800","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:50:21Z","timestamp":1776837021000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review"],"prefix":"10.3389","volume":"9","author":[{"given":"Bruno Enagnon","family":"Lokonon","sequence":"first","affiliation":[{"name":"Centre Suisse de Recherches Scientifiques en C\u00f4te d'Ivoire (CSRS)","place":["Abidjan, C\u00f4te d'Ivoire"]},{"name":"Laboratoire de Biomath\u00e9matiques et d\u2019Estimations Foresti\u00e8res, Universit\u00e9 d'Abomey-Calavi","place":["Cotonou, Benin"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e8ton Calmette Ariane","family":"Houetohossou","sequence":"additional","affiliation":[{"name":"Laboratoire de Biomath\u00e9matiques et d\u2019Estimations Foresti\u00e8res, Universit\u00e9 d'Abomey-Calavi","place":["Cotonou, Benin"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno Am\u00e8djiko","family":"Tchede","sequence":"additional","affiliation":[{"name":"Laboratoire de Biomath\u00e9matiques et d\u2019Estimations Foresti\u00e8res, Universit\u00e9 d'Abomey-Calavi","place":["Cotonou, Benin"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard B.","family":"Yapi","sequence":"additional","affiliation":[{"name":"Centre Suisse de Recherches Scientifiques en C\u00f4te d'Ivoire (CSRS)","place":["Abidjan, C\u00f4te d'Ivoire"]},{"name":"Centre d\u2019Entomologie M\u00e9dicale et V\u00e9t\u00e9rinaire, Universit\u00e9 Alassane Ouattara","place":["Bouak\u00e9, C\u00f4te d'Ivoire"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aur\u00e9lie","family":"Cailleau","sequence":"additional","affiliation":[{"name":"Centre Suisse de Recherches Scientifiques en C\u00f4te d'Ivoire (CSRS)","place":["Abidjan, C\u00f4te d'Ivoire"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel T.","family":"Haydon","sequence":"additional","affiliation":[{"name":"School of Biodiversity, One Health and Veterinary Medicine, College of Medical Veterinary and Life Sciences, University of Glasgow","place":["Glasgow, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bassirou","family":"Bonfoh","sequence":"additional","affiliation":[{"name":"Centre Suisse de Recherches Scientifiques en C\u00f4te d'Ivoire (CSRS)","place":["Abidjan, C\u00f4te d'Ivoire"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"107028","DOI":"10.1016\/j.ssci.2025.107028","article-title":"Artificial intelligence adoption challenges from healthcare providers\u2019 perspectives: a comprehensive review and future directions","volume":"193","author":"Abdelwanis","year":"2026","journal-title":"Saf. 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