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A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.<\/jats:p>","DOI":"10.1007\/s00256-021-03830-8","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:03:16Z","timestamp":1630447396000},"page":"315-329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches"],"prefix":"10.1007","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8733-131X","authenticated-orcid":false,"given":"Benjamin","family":"Fritz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4456-3043","authenticated-orcid":false,"given":"Jan","family":"Fritz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"3830_CR1","doi-asserted-by":"publisher","first-page":"A1","DOI":"10.1016\/j.mri.2019.12.006","volume":"68","author":"JC Gore","year":"2020","unstructured":"Gore JC. 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Jan Fritz: Jan Fritz received institutional research support from Siemens AG, BTG International, Zimmer Biomed, DePuy Synthes, QED, and SyntheticMR; is a scientific advisor for Siemens AG, SyntheticMR, GE Healthcare, QED, BTG, ImageBiopsy Lab, Boston Scientific, and Mirata Pharma; and has shared patents with Siemens Healthcare and Johns Hopkins University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}