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A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently \u201cinterpretable,\u201d whereas post hoc \u201cexplainability\u201d methods can be used for other models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Here we sought to summarize the state\u2010of\u2010the\u2010art of interpretable machine learning for dementia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia\u2010related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient\u2010specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alz.12948","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T15:04:00Z","timestamp":1675436640000},"page":"2135-2149","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["Interpretable machine learning for dementia: A systematic review"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3819-1634","authenticated-orcid":false,"given":"Sophie A.","family":"Martin","sequence":"first","affiliation":[{"name":"Centre for Medical Image Computing Department of Computer Science University College London  London UK"},{"name":"Dementia Research Centre Queen Square Institute of Neurology University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7803-5682","authenticated-orcid":false,"given":"Florence J.","family":"Townend","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing Department of Computer Science University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3543-3706","authenticated-orcid":false,"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing Department of Computer Science University College London  London UK"},{"name":"Dementia Research Centre Queen Square Institute of Neurology University College London  London UK"},{"name":"Amsterdam UMC, Department of Radiology &amp; Nuclear Medicine Vrije Universiteit  Amsterdam Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1908-5588","authenticated-orcid":false,"given":"James H.","family":"Cole","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing Department of Computer Science University College London  London UK"},{"name":"Dementia Research Centre Queen Square Institute of Neurology University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awn239"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2019.00220"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2870052"},{"key":"e_1_2_11_5_1","unstructured":"TjoaE GuanC A Survey on Explainable Artificial Intelligence (XAI): towards Medical XAI.2015."},{"key":"e_1_2_11_6_1","first-page":"520","volume-title":"Building Trust in AI: Opportunities and Challenges for Cardiac Imaging","author":"Sengupta PP","year":"2021"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/medethics\u20102019\u2010105935"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190043"},{"key":"e_1_2_11_9_1","unstructured":"DasA RadP Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey.2020."},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3408292"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50334-5_4"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPBIOMED.2021.105111"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"RibeiroMT SinghS GuestrinC \"Why should i trust you?\" explaining the predictions of any classifier.Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016;1135\u20101144. doi:10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_2_11_15_1","unstructured":"GDPR.Guide to the General Data Protection Regulation. 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