{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T06:13:14Z","timestamp":1779257594039,"version":"3.51.4"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Worldwide, tuberculosis (TB) continues to be an important cause of human morbidity and mortality, particularly in developing countries where drug surveillance and rapid detection of resistance to anti-TB drugs is uncommon and the lack of proper healthcare systems often leads to incomplete treatment and spread of MTB strains. The present paper discusses the potential role of artificial intelligence (AI) in improving TB diagnosis, management, and control in African countries, where conventional diagnostic approaches remain predominant and often insufficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A narrative review of current challenges in TB management in African settings was conducted, focusing on limitations of existing diagnostic tools and the emerging contributions of AI-based technologies in healthcare. The review demonstrates how machine learning algorithms and computer-aided systems could be integrated into TB programs to enhance clinical decision-making and surveillance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Conventional TB diagnostic methods such as the tuberculin test, radiography, and microscopic examination show limited accuracy and efficiency, contributing to ongoing transmission and poor treatment outcomes in many African countries. AI innovations have demonstrated improved performance in disease detection and prediction across various health domains, offering time-saving, resource-efficient, and scalable solutions. Applied to TB, AI could support clinicians in diagnosis, forecast treatment outcomes, and strengthen public health strategies aimed at controlling MTB spread.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI holds significant promise for enhancing TB control efforts in African countries by improving diagnostic precision, clinical decision-making, and surveillance capacities. Integrating AI into national TB programs could promote more effective and efficient disease management, ultimately contributing to reduced morbidity and mortality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1816634","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:44:59Z","timestamp":1779255899000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for tuberculosis management in Africa: opportunities, challenges, and implementation"],"prefix":"10.3389","volume":"14","author":[{"given":"Imane","family":"Chaoui","sequence":"first","affiliation":[{"name":"Department of Life Sciences, Centre National de l\u2019Energie, des Sciences et des Techniques Nucl\u00e9aires (CNESTEN)","place":["Rabat, Morocco"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Amine","family":"Koulali","sequence":"additional","affiliation":[{"name":"Laboratoire de Mod\u00e9lisation et Calcul Scientifique (LMCS), ENSAO, Mohammed First University","place":["Oujda, Morocco"]},{"name":"College of Computing, Mohammed VI Polytechnic University","place":["Ben Guerir, Morocco"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Onesime","family":"Mbulayi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Statistics and Informatics, University of Kinshasa","place":["Kinshasa, Democratic Republic of Congo"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saint-Jean","family":"Djungu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Statistics and Informatics, University of Kinshasa","place":["Kinshasa, Democratic Republic of Congo"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loukia","family":"Aketi","sequence":"additional","affiliation":[{"name":"Cardio-Pulmonary and Infectious Diseases Unit, Department of Pediatrics, University of Kinshasa","place":["Kinshasa, Democratic Republic of Congo"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdou Azaque","family":"Zour\u00e9","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Science de la Sant\u00e9, Centre National de Recherche Scientifique et Technologique (IRSS\/CNRST)","place":["Ouagadougou, Burkina Faso"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamaleddine","family":"Bourkadi","sequence":"additional","affiliation":[{"name":"Pneumology Department, Moulay Youssef Hospital - Ibn Sina University Hospital","place":["Rabat, Morocco"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahya","family":"Tayalati","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Sciences, University Mohammed V","place":["Rabat, Morocco"]},{"name":"School of Applied and Engineering Physics, Mohammed VI Polytechnic University","place":["Ben Guerir, Morocco"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"El Mzibri","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, Centre National de l\u2019Energie, des Sciences et des Techniques Nucl\u00e9aires (CNESTEN)","place":["Rabat, Morocco"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"AI4TB Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"ref1","volume-title":"Global Tuberculosis Report 2024","year":"2024"},{"key":"ref2","volume-title":"Global Tuberculosis Report 2023","year":""},{"key":"ref3","doi-asserted-by":"publisher","first-page":"380","DOI":"10.3201\/eid1303.061400","article-title":"Worldwide emergence of extensively drug-resistant tuberculosis","volume":"13","author":"Shah","year":"2007","journal-title":"Emerg Infect Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1378\/chest.08-2427","article-title":"Emergence of new forms of totally drug-resistant tuberculosis bacilli: super extensively drug-resistant tuberculosis or totally drug-resistant strains in Iran","volume":"136","author":"Velayati","year":"2009","journal-title":"Chest"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/S0140-6736(14)60844-8","article-title":"Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990\u20132013: a systematic analysis for the global burden of disease study 2013","volume":"384","author":"Murray","year":"2014","journal-title":"Lancet"},{"key":"ref6","volume-title":"Major Infectious Diseases","author":"Bloom","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1186\/s12889-020-08875-3","article-title":"African-led health research and capacity building- is it working?","volume":"20","author":"Kasprowicz","year":"2020","journal-title":"BMC Public Health"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"9892152","DOI":"10.34133\/2021\/9892152","article-title":"Recent development in X-Ray imaging technology: future and challenges","volume":"2021","author":"Ou","year":"2021","journal-title":"Research (Wash D C)"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s12962-023-00485-1","article-title":"Health system productivity in sub-Saharan Africa: tuberculosis control in high burden countries","volume":"21","author":"Atake","year":"2023","journal-title":"Cost Eff Resour Alloc"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"388","DOI":"10.3390\/clinpract14020030","article-title":"Evolution of laboratory diagnosis of tuberculosis","volume":"14","author":"Zaporojan","year":"2024","journal-title":"Clin Pract"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S1473-3099(15)00499-5","article-title":"The transmission of Mycobacterium tuberculosis in high burden settings","volume":"16","author":"Yates","year":"2016","journal-title":"Lancet Infect Dis"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1186\/s12916-018-1180-x","article-title":"Global variation in bacterial strains that cause tuberculosis disease: a systematic review and meta-analysis","volume":"16","author":"Wiens","year":"2018","journal-title":"BMC Med"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e55641","DOI":"10.2196\/55641","article-title":"Comparing the output of an artificial intelligence algorithm in detecting radiological signs of pulmonary tuberculosis in digital chest X-rays and their smartphone-captured photos of X-ray films: retrospective study","volume":"8","author":"Ridhi","year":"2024","journal-title":"JMIR Form Res"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.acra.2019.10.003","article-title":"Artificial intelligence, radiology, and tuberculosis: a review","volume":"27","author":"Kulkarni","year":"2020","journal-title":"Acad Radiol"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00330-022-08997-9","article-title":"Machine learning and radiomics for the prediction of multidrug resistance in cavitary pulmonary tuberculosis: a multicenter study","volume":"33","author":"Li","year":"2023","journal-title":"Eur Radiol"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"7082","DOI":"10.3390\/app13127082","article-title":"Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges","volume":"13","author":"Aldoseri","year":"2023","journal-title":"Appl Sci"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"935080","DOI":"10.3389\/fmed.2022.935080","article-title":"The application of artificial intelligence in the diagnosis and drug resistance prediction of pulmonary tuberculosis","volume":"9","author":"Liang","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1225438","DOI":"10.3389\/fmicb.2023.1225438","article-title":"Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine","volume":"14","author":"Doh\u00e1l","year":"2023","journal-title":"Front Microbiol"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"15000","DOI":"10.1038\/s41598-019-51503-3","article-title":"Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems","volume":"9","author":"Qin","year":"2019","journal-title":"Sci Rep"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"757","DOI":"10.3390\/ijerph13080757","article-title":"Seasonality and trend forecasting of tuberculosis prevalence data in eastern cape, South Africa, using a hybrid model","volume":"13","author":"Azeez","year":"2016","journal-title":"Int J Environ Res Public Health"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e0000084","DOI":"10.1371\/journal.pdig.0000084","article-title":"Application of ARIMA, and hybrid ARIMA models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana counties, Kenya","volume":"2","author":"Siamba","year":"2023","journal-title":"PLOS Digit Health"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"S293","DOI":"10.1093\/cid\/ciy611","article-title":"Levofloxacin pharmacokinetics\/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis","volume":"67","author":"Deshpande","year":"2018","journal-title":"Clin Infect Dis"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1186\/s12879-023-08460-0","article-title":"Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores from a tuberculosis prevalence survey in Zambia and South Africa","volume":"23","author":"Ngosa","year":"2023","journal-title":"BMC Infect Dis"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e0000535","DOI":"10.1371\/journal.pdig.0000535","article-title":"A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia","volume":"4","author":"Linsen","year":"2025","journal-title":"PLOS Digit Health."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1476866","DOI":"10.3389\/fpubh.2024.1476866","article-title":"Comparative effectiveness of chest ultrasound, chest X-ray and computer-aided diagnostic (CAD) for tuberculosis diagnosis in low-resource setting: study protocol for a cross-sectional study from Ethiopia","volume":"12","author":"Guido","year":"2024","journal-title":"Front Public Health"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1613\/jair.1.12162","article-title":"Mapping the landscape of artificial intelligence applications against COVID-19","volume":"69","author":"Bullock","year":"2020","journal-title":"J Artif Intell Res"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.pt.2018.06.006","article-title":"Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology","volume":"34","author":"Fornace","year":"2018","journal-title":"Trends Parasitol"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1816634\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:45:01Z","timestamp":1779255901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1816634\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":27,"alternative-id":["10.3389\/fpubh.2026.1816634"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1816634","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]},"article-number":"1816634"}}