{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T12:38:16Z","timestamp":1792586296978,"version":"4.5.0"},"reference-count":97,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Intracranial electroencephalography (iEEG) provides direct recordings of cortical activity and remains central to the presurgical evaluation of drug\u2010resistant focal epilepsy. However, the high dimensionality and complexity of iEEG data increasingly challenge conventional visual interpretation. Artificial intelligence (AI) methods have accelerated quantitative analyses in electrophysiology, supporting localization, outcome prediction, and therapeutic decision\u2010making in epilepsy. This article reviews recent advances in the application of AI to iEEG in epilepsy and in particular stereo\u2010EEG, although the concepts discussed are broadly relevant across iEEG modalities. Although many of these approaches have shown encouraging results, the current evidence remains largely preliminary, with most studies being retrospective, single\u2010center investigations that lack independent external validation. AI\u2010based integration of iEEG with multimodal magnetic resonance imaging shows promise for improving surgical planning in drug\u2010resistant focal epilepsy. Concurrently, early studies suggest that AI\u2010driven iEEG analysis may enhance epileptogenic zone identification by detecting subtle electrophysiological signatures, network interactions, and temporal dynamics. These multimodal frameworks emphasize distributed epileptic networks rather than isolated focal abnormalities. AI is also reshaping how iEEG\u2010guided stimulation is designed and interpreted. Data\u2010driven approaches have the potential to optimize stimulation parameters, predict network responses, and clarify mechanisms underlying diagnostic and therapeutic stimulation, laying the groundwork for adaptive, patient\u2010specific neuromodulation. Beyond short\u2010term epilepsy monitoring, AI applications increasingly incorporate continuous sensing, longitudinal network modeling, and integration with implantable neurotechnology platforms. Although these developments require prospective multicenter studies with external validation before widespread clinical implementation, advances in multimodal data integration, interpretable modeling, and continuous sensing position AI\u2010enhanced iEEG as an emerging platform for precision brain network medicine for epilepsy.<\/jats:p>","DOI":"10.1002\/epi.70461","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T11:39:14Z","timestamp":1792582754000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence applications in intracranial electroencephalography for epilepsy management: Insights from\n                    <scp>AI<\/scp>\n                    in Epilepsy 2025"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6064-1529","authenticated-orcid":false,"given":"Birgit","family":"Frauscher","sequence":"first","affiliation":[{"name":"Department of Neurology Duke University Medical Center  Durham North Carolina USA"},{"name":"Department of Biomedical Engineering Duke Pratt School of Engineering  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Furqan","family":"Afzal","sequence":"additional","affiliation":[{"name":"NeuroPace  Mountain View California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn A.","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Provenza","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Baylor College of Medicine  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer A.","family":"Sheth","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Baylor College of Medicine  Houston Texas USA"},{"name":"Cain Laboratories Duncan Neurological Research Institute, Texas Children's Hospital  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7142-0684","authenticated-orcid":false,"given":"Rachel June","family":"Smith","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department University of Alabama at Birmingham  Birmingham Alabama USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0144-3746","authenticated-orcid":false,"given":"John","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Technology Rochester Institute of Technology  Rochester New York USA"},{"name":"Department of Neurology University of Rochester  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"1","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/epd2.20190","article-title":"Learn how to interpret and use intracranial EEG findings","volume":"26","author":"Frauscher B","year":"2024","journal-title":"Epileptic Disord"},{"issue":"6","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41582-024-00965-9","article-title":"Artificial intelligence in epilepsy\u2014applications and pathways to the clinic","volume":"20","author":"Lucas A","year":"2024","journal-title":"Nat Rev Neurol"},{"issue":"7","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1093\/brain\/awz125","article-title":"Characterizing the role of the structural connectome in seizure 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