{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:28:20Z","timestamp":1790234900868,"version":"4.1.0"},"reference-count":203,"publisher":"AIP Publishing","issue":"4","license":[{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["688279"],"award-info":[{"award-number":["688279"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"abstract":"<jats:p>Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using \u201cartificial intelligence\u201d and \u201cbrain\u201d as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.<\/jats:p>","DOI":"10.1063\/5.0011697","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T10:45:36Z","timestamp":1602585936000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":166,"title":["Artificial intelligence for brain diseases: A systematic review"],"prefix":"10.1063","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7808-4123","authenticated-orcid":false,"given":"Alice","family":"Segato","sequence":"first","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano 1 , Milan 20133, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-7156","authenticated-orcid":false,"given":"Aldo","family":"Marzullo","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Calabria 2 , Rende 87036, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-0834","authenticated-orcid":false,"given":"Francesco","family":"Calimeri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Calabria 2 , Rende 87036, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8819-2734","authenticated-orcid":false,"given":"Elena","family":"De Momi","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano 1 , Milan 20133, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"2024031517001419700_c1","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1093\/neuros\/nyx384","article-title":"Natural and artificial intelligence in neurosurgery: A systematic review","volume":"83","year":"2018","journal-title":"Neurosurgery"},{"key":"2024031517001419700_c2","doi-asserted-by":"publisher","first-page":"e1002707","DOI":"10.1371\/journal.pmed.1002707","article-title":"Deep learning and artificial intelligence in radiology: Current applications and future directions","volume":"15","year":"2018","journal-title":"PLoS Med."},{"key":"2024031517001419700_c3","doi-asserted-by":"publisher","first-page":"3629","DOI":"10.1007\/s00330-015-3770-8","article-title":"Cost-effectiveness modelling in diagnostic imaging: A stepwise approach","volume":"25","year":"2015","journal-title":"Eur. 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Cardiol. Rep."},{"key":"2024031517001419700_c198","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1136\/jamia.1996.97084510","article-title":"Artificial intelligence in medicine: The challenges ahead","volume":"3","year":"1996","journal-title":"J. Am. Med. Inf. 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