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Limited access to neurologists, diagnostic tools, and antiseizure medications, combined with persistent stigma, contribute to poor outcomes, including premature mortality. Artificial intelligence (AI) offers potential to address these gaps through scalable, low\u2010cost solutions for diagnosis, investigation, management, and monitoring.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review examines three recent and complementary AI applications in epilepsy care for LMICs: a smartphone\u2010based diagnostic tool for convulsive epilepsy developed using population\u2010based data from five sub\u2010Saharan African countries; an automated EEG interpretation system based on a deep learning model (SCORE\u2010AI) validated across multicenter datasets; and a wearable\u2010based deep learning model for detecting generalized convulsive seizures using low\u2010cost smartwatches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The smartphone diagnostic tool achieved area under the curve (AUC) 0.92\u20130.95 with sensitivity 85.0%\u201397.5% for identifying epilepsy with convulsive seizures using eight binary clinical features. SCORE\u2010AI demonstrated expert\u2010level performance (AUC 0.89\u20130.96, accuracy 85%\u201392%) for automated EEG classification across multiple validation datasets. The wearable seizure detection algorithm achieved 96% sensitivity with approximately one false alarm per 8\u2009days. All three solutions were designed for deployment on widely accessible platforms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010driven approaches demonstrate feasibility for addressing diagnostic and monitoring gaps in resource\u2010limited settings. However, implementation faces substantial challenges including infrastructure constraints, limited digital literacy, ethical considerations, and sociocultural factors. Successful deployment requires validation with large locally relevant datasets, context\u2010adapted solutions, task\u2010sharing strategies, implementation research, appropriate regulatory frameworks\/certification, and community engagement to reduce the global epilepsy care gap.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ene.70740","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:17:31Z","timestamp":1788859051000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Improving the Management of People With Epilepsy in Low\u2010and\u2010Middle Income Countries"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7775-6576","authenticated-orcid":false,"given":"Philippe","family":"Ryvlin","sequence":"first","affiliation":[{"name":"Department of Clinical Neurosciences \u2013 Member of the European Reference 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