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However, there currently lacks an appropriate assessment of clinical utility and study rigor of current AI\/ML\u2010driven models that are targeted toward supporting decision\u2010making within the clinical workup in epilepsy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically reviewed and examined the ability of current AI\/ML\u2010driven models in MRI across four main applications within the clinical workup(s) for epilepsy: (1) diagnosis, (2) temporal lobe epilepsy lateralization, (3) lesion (focal cortical dysplasia) localization, and (4) postsurgical outcome prediction. We additionally assessed the risk of bias for each study model. Studies that employed AI\/ML classification models trained on any MRI modality or sequence type were selected for qualitative assessment; those reporting accuracy rates were subsequently included in the meta\u2010analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 3227 searched articles, we identified 159 studies (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200926 732 participants) for qualitative evaluation and 127 studies (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200920 456) for inclusion in the meta\u2010analysis. Our results reveal that AI\/ML on MRI could accurately distinguish epilepsy patients from healthy controls (overall accuracy = .87, 95% confidence interval [CI] = .85\u2013.89), lateralize temporal lobe epilepsy (.90, 95% CI = .87\u2013.93), localize epileptogenic lesions (.82, 95% CI = .74\u2013.87), and predict postsurgical seizure freedom (.83, 95% CI = .78\u2013.87). However, systematic assessment indicated a very high risk of bias in the literature, suggestive of overly optimistic performance estimates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>Although our results support overall high accuracy of AI\/ML models in epilepsy diagnostics and prognostics, the literature remains susceptible to bias in participant recruitment and validation methods. Furthermore, most models were limited by study architecture that demands strict adherence to nonstandard, highly specific data acquisition and processing protocols that cannot be easily deployed for clinical implementation. We encourage closer interdisciplinary collaboration between clinical and scientific groups to improve validation studies, and outline suggested recommendations for future study design, analysis, and reporting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/epi.70254","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:41:11Z","timestamp":1780666871000},"page":"4194-4206","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta\u2010analysis of four clinical applications"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6332-9935","authenticated-orcid":false,"given":"Judy","family":"Chen","sequence":"first","affiliation":[{"name":"Multimodal Imaging and Connectome Analysis Laboratory (MICA), McConnell Brain Imaging Centre and Centre for Excellence in Epilepsy at the Neuro, Montreal Neurological Institute and Hospital McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ella","family":"Sahlas","sequence":"additional","affiliation":[{"name":"Multimodal Imaging and Connectome Analysis Laboratory (MICA), McConnell Brain Imaging Centre and Centre for Excellence in Epilepsy at the Neuro, Montreal Neurological Institute and Hospital McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yigu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Multimodal Imaging and Connectome Analysis Laboratory (MICA), McConnell Brain Imaging Centre and Centre for Excellence in Epilepsy at the Neuro, Montreal Neurological Institute and Hospital McGill University  Montreal Quebec 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Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Caciagli","sequence":"additional","affiliation":[{"name":"Department of Neurology, Inselspital, Sleep\u2010Wake\u2010Epilepsy Center, Bern University Hospital University of Bern  Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristides","family":"Hadjinicolaou","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire Sainte\u2010Justine Universit\u00e9 de Montr\u00e9al  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander G.","family":"Weil","sequence":"additional","affiliation":[{"name":"Centre Hospitalier Universitaire Sainte\u2010Justine Universit\u00e9 de Montr\u00e9al  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy W.","family":"Dudley","sequence":"additional","affiliation":[{"name":"Montreal Children's Hospital McGill University  Montreal 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