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In clinical practice, diagnosis and severity assessments rely mainly on visual clinical inspections (ie, on subjective expert opinion). With clinical videos commonly acquired as part of examinations, novel data\u2010driven models have emerged that use machine learning (ML) and deep learning (DL) algorithms to capture human actions and recognize their characteristics, showing promise as new tools in clinical workflows. This review seeks to provide a comprehensive examination of video\u2010based, data\u2010driven models for movement disorders, including tremor, dystonia, myoclonus, chorea, tics, Parkinson's disease, and ataxia. We explore literature from 2006 to 2024 in a variety of scientific databases, with different video modalities including red\u2010green\u2010blue video of different frame rates, depth video, marker\u2010based approaches, multi\u2010perspective approaches, and multimodal video. We discover a significant trend in studies favoring pose estimation methods, with newer studies incorporating real\u2010time methods and end\u2010to\u2010end DL architectures, and usability is steadily increasing and rapidly approaching expert\u2010level performance. Likewise, we present the main limitations in the current approaches, such as limited public sources of data, lack of standardized metrics, and patient privacy. Taking inspiration from other fields, in medicine and otherwise, we propose possible future research directions including explainable artificial intelligence techniques, privacy\u2010preserving devices and modeling techniques, and better metric guidelines. \u00a9 2025 The Author(s).\n                    <jats:italic>Movement Disorders<\/jats:italic>\n                    published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.\n                  <\/jats:p>","DOI":"10.1002\/mds.30327","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T10:49:27Z","timestamp":1755254967000},"page":"2046-2066","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Video\u2010Based Data\u2010Driven Models for Diagnosing Movement Disorders: Review and Future Directions"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8956-0316","authenticated-orcid":false,"given":"Rafael","family":"Mart\u00ednez\u2010Garc\u00eda\u2010Pe\u00f1a","sequence":"first","affiliation":[{"name":"Department of Neurology University of Groningen, University Medical Centre Groningen (UMCG)  Groningen The Netherlands"},{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2864-4225","authenticated-orcid":false,"given":"Lisette H.","family":"Koens","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Groningen, University Medical Centre Groningen (UMCG)  Groningen The Netherlands"},{"name":"Department of Neurology and Clinical Neurophysiology, Martini Ziekenhuis  Groningen The Netherlands"},{"name":"Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen (UMCG)  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6552-2596","authenticated-orcid":false,"given":"George","family":"Azzopardi","sequence":"additional","affiliation":[{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5783-571X","authenticated-orcid":false,"given":"Marina A.J.","family":"Tijssen","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Groningen, University Medical Centre Groningen (UMCG)  Groningen The Netherlands"},{"name":"Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen (UMCG)  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.22360"},{"key":"e_1_2_11_3_1","series-title":"Neurologic Aspects of Systemic Disease Part III","first-page":"1773","volume-title":"Handbook of Clinical Neurology","author":"Singhal BS","year":"2014"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.28762"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41531-018-0058-0"},{"issue":"4","key":"e_1_2_11_6_1","first-page":"213","article-title":"Cerebral Palsy: An Overview","volume":"101","author":"Vitrikas K","year":"2020","journal-title":"Am Fam Physician"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22340"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejpn.2017.04.1333"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mdc3.13974"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2021.07.033"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12121735"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3197671"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.4103\/singaporemedj.SMJ-2023-189"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare12040439"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102914"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102952"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2024.5326"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.4103\/1673-5374.382249"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n160"},{"key":"e_1_2_11_20_1","unstructured":"ZhuY LiX LiuC et al.A Comprehensive Study of Deep Video Action Recognition; 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