{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:06:16Z","timestamp":1792807576072,"version":"4.5.0"},"reference-count":50,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real\u2010time data. This review explores the current applications and future potential of multimodal AI across healthcare, with a particular focus on orthopaedic surgery. In presurgical planning, multimodal AI has demonstrated significant improvements in diagnostic accuracy and risk prediction, with studies reporting an Area under the receiving operator curve presenting good to excellent performance across various orthopaedic conditions. Intraoperative applications leverage advanced imaging and tracking technologies to enhance surgical precision, while postoperative care has been advanced through continuous patient monitoring and early detection of complications. Despite these advances, significant challenges remain in data integration, standardisation, and privacy protection. Technical solutions such as federated learning (allowing decentralisation of models) and edge computing (allowing data analysis to happen on site or closer to site instead of multipurpose datacenters) are being developed to address these concerns while maintaining compliance with regulatory frameworks. As this field continues to evolve, the integration of multimodal AI promises to advance personalised medicine, improve patient outcomes, and transform healthcare delivery through more comprehensive and nuanced analysis of patient data.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Level of Evidence:<\/jats:bold>\n                    Level V.\n                  <\/jats:p>","DOI":"10.1002\/ksa.12657","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T12:31:34Z","timestamp":1742733094000},"page":"2269-2275","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-685X","authenticated-orcid":false,"given":"Felix C.","family":"Oettl","sequence":"first","affiliation":[{"name":"Department of Orthopedic Surgery, Balgrist University Hospital University of Z\u00fcrich Zurich Switzerland"},{"name":"Hospital for Special Surgery New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B\u00e1lint","family":"Zsidai","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg Gothenburg Sweden"},{"name":"Sahlgrenska Sports Medicine Center G\u00f6teborg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob F.","family":"Oeding","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy University of Gothenburg Gothenburg Sweden"},{"name":"Mayo Clinic Alix School of Medicine Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael T.","family":"Hirschmann","sequence":"additional","affiliation":[{"name":"Department of 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