{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T09:53:59Z","timestamp":1793872439644,"version":"4.6.1"},"reference-count":74,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"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. Neurol."],"abstract":"<jats:p>Epilepsy affects millions of people worldwide, driving the need for advanced methods to monitor patients\u2019 health and seizure activity. Recent advances in wearable technologies have enabled continuous collection of physiological data to support real-time seizure detection in the real-world. This review presents a targeted synthesis of 23 studies evaluating wearable devices and their associated artificial intelligence (AI) algorithms for automated seizure detection. Both wrist- and ear-based systems demonstrate high sensitivity, with performance influenced by device design, signal reliability, and analytic approach. The main challenges include reducing false alarms and maintaining data integrity during everyday use. More recent studies highlight the ability to anticipate seizures before they occur, marking a promising step toward improving safety and well-being for people living with epilepsy. Ongoing efforts to identify reliable physiological markers and to evaluate device performance across diverse populations are key to integrating wearable technologies for seizure detection into routine medical care.<\/jats:p>","DOI":"10.3389\/fneur.2026.1756895","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:30:08Z","timestamp":1773034208000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence in wearable seizure detection devices: current technologies and future directions"],"prefix":"10.3389","volume":"17","author":[{"given":"Tiffany Jiaqi","family":"Ho","sequence":"first","affiliation":[{"name":"Department of Molecular and Cell Biology, University of California, Berkeley","place":["Berkeley, CA, United States"]},{"name":"Department of Neurology, University of California, San Francisco","place":["San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bridget Elaine LaMonica","family":"Ostrem","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of California, San Francisco","place":["San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James Michael","family":"Hillis","sequence":"additional","affiliation":[{"name":"Department of Neurology, Massachusetts General Hospital","place":["Boston, MA, United States"]},{"name":"Mass General Brigham AI, Mass General Brigham","place":["Boston, MA, United States"]},{"name":"Harvard Medical School","place":["Boston, MA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref1","year":"2025"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1111\/epi.18338","article-title":"Updated classification of epileptic seizures: 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