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In addition, the last decade has witnessed continued developments in MRI\u2010based computer\u2010aided machine\u2010learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging\u2010derived prognostic markers, including response to anti\u2010seizure medication, post\u2010surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person\u2010centered care.<\/jats:p>","DOI":"10.1111\/epi.17989","type":"journal-article","created":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T06:54:34Z","timestamp":1713596074000},"page":"64-71","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The use of automated and <scp>AI<\/scp>\u2010driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia"],"prefix":"10.1111","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9358-5703","authenticated-orcid":false,"given":"Andrea","family":"Bernasconi","sequence":"first","affiliation":[{"name":"Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravnoor 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