{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:51:51Z","timestamp":1792079511875,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>Musculoskeletal care is being reshaped by population aging, multimorbidity, frailty, osteoporosis, sarcopenia, polypharmacy and increasing vulnerability to postoperative loss of independence. Artificial intelligence (AI) has entered orthopaedic research, but its clinical translation remains constrained. Many models address isolated diagnostic, imaging or short\u2010term prediction tasks rather than the longitudinal care trajectories that determine outcomes in older musculoskeletal patients. The central limitation is therefore not simply whether older adults are represented in datasets, but whether AI systems are valid across the continuum of geriatric orthopaedic care. This review argues for a transition from task\u2010specific orthopaedic AI toward continuum\u2010of\u2010care AI for the aging musculoskeletal patient. Upstream, radiomics, longitudinal osteoarthritis phenotyping, opportunistic computed tomography (CT)\u2010based skeletal assessment and AI\u2010augmented dual\u2010energy X\u2010ray absorptiometry (DXA) modelling may improve prediction of disease progression, fracture risk, treatment failure and surgical timing. At the point of intervention, AI\u2010assisted arthroplasty planning may help move total knee arthroplasty from generic alignment targets toward phenotype\u2010specific reconstruction strategies that account for constitutional alignment, bone morphology, ligament envelope, neuromuscular control, frailty and functional reserve. Perioperatively, automated frailty and sarcopenia phenotyping, wearable gait analysis and multimodal temporal models integrating imaging, electronic health records, laboratory data, medications, patient\u2010reported outcomes and wearable signals may support dynamic risk prediction. After discharge, continuous monitoring of mobility, sleep, pain trajectories, wound\u2010related symptoms and rehabilitation adherence may help close the high\u2010risk surveillance gap during the 90\u2010day postoperative episode. However, clinical adoption requires more than high discrimination. Explainable AI should be understood as one component of a broader trust framework that also includes calibration, uncertainty quantification, decision\u2010curve analysis, external and prospective validation, workflow integration, post\u2010deployment monitoring and accountability. The future of orthopaedic AI is not simply better diagnosis or more precise surgery, but longitudinal, multimodal support across the full care continuum: predicting progression, selecting treatment, personalizing surgery, optimizing perioperative care, monitoring recovery and preserving mobility and independence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>Level IV.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ksa.70647","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:31:13Z","timestamp":1792078273000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence across the continuum of care for the aging musculoskeletal patient: From longitudinal phenotyping to personalized surgery and recovery monitoring"],"prefix":"10.1002","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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balint","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"},{"name":"Department of Orthopedics Sk\u00e5ne University Hospital Malm\u00f6\/Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James A.","family":"Pruneski","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery Tripler Army Medical Center Honolulu Hawaii USA"}],"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umile Giuseppe","family":"Longo","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario Campus Bio\u2010Medico Roma Italy"},{"name":"Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery Universit\u00e0 Campus Bio\u2010Medico di Roma Roma Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael T.","family":"Hirschmann","sequence":"additional","affiliation":[{"name":"University Department of Orthopedic Surgery and Traumatology Kantonsspital Baselland Bruderholz Switzerland"},{"name":"Department of Clinical Research, Research Group Michael T. Hirschmann, Regenerative Medicine and Biomechanics University of Basel Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6199-908X","authenticated-orcid":false,"given":"Ro","family":"Du Hyun","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Seoul National University Hospital Seoul Republic of Korea"},{"name":"Department of Orthopedics Seoul National University College of Medicine Seoul Republic of Korea"},{"name":"Healthcare AI Research Institute, Seoul National University Hospital Seoul Republic of Korea"},{"name":"CONNECTEVE Co., Ltd. Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristian","family":"Samuelsson","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"}]},{"name":"ESSKA Artificial Intelligence Committee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s00198-026-08000-x","article-title":"Revised Swedish FRAX models and the establishment of age\u2010dependent intervention thresholds","volume":"37","author":"Axelsson KF","year":"2026","journal-title":"Osteoporos Int"},{"key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1007\/s00402-021-04051-3","article-title":"Assistive technologies in knee arthroplasty: fashion or evolution? 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