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AI-enabled triage systems enhance patient flow efficiency, while automated scheduling, symptom checkers, and AI-powered virtual assistants streamline pre-visit interactions. In MSK radiographic diagnostics, AI augments imaging interpretation, enabling automated fracture detection, opportunistic screening, and quantitative imaging, improving diagnostic accuracy and standardization. Preoperative planning solutions facilitate implant templating, surgical navigation, and patient-specific instrumentation, reducing variability and enhancing surgical precision. Concurrently, digital scribes and AI-driven documentation tools alleviate administrative overhead, mitigating clinician burnout and enabling refocused patient engagement. Predictive analytics optimize treatment pathways by leveraging multimodal patient data for risk stratification and personalized decision support. However, algorithmic bias, model generalizability, regulatory hurdles, and legal ambiguities present substantial implementation barriers, necessitating rigorous validation, adaptive governance, and seamless clinical integration. The U.S. and EU regulatory landscapes diverge in their approaches to AI oversight, with the former favoring expedited market access and the latter imposing stringent compliance mandates under the EU AI Act. AI\u2019s integration into MSK care demands robust validation frameworks, standardized interoperability protocols, and dynamic regulatory pathways balancing safety and innovation. Emerging generalist foundation models, open-source large language models (LLMs), and specialized AI-driven medical applications herald a paradigm shift toward precision MSK care. These innovations will require prospective clinical validation to ensure patient benefit and mitigate risk. Addressing ethical considerations, ensuring equitable access, and fostering interdisciplinary collaboration remain paramount in translating AI\u2019s potential into tangible improvements in MSK healthcare delivery.<\/jats:p>","DOI":"10.1177\/15563316251341321","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T01:27:09Z","timestamp":1748654829000},"page":"331-341","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Advancing Musculoskeletal Care Using AI and Digital Health Applications: A Review of Commercial Solutions"],"prefix":"10.1177","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4128-5431","authenticated-orcid":false,"given":"Johannes","family":"Pawelczyk","sequence":"first","affiliation":[{"name":"Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6488-1009","authenticated-orcid":false,"given":"Moritz","family":"Kraus","sequence":"additional","affiliation":[{"name":"Department of Traumatology, University Hospital Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Voigtlaender","sequence":"additional","affiliation":[{"name":"Virtual Diagnostics Unit, QuantCo, Inc., Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Siebenlist","sequence":"additional","affiliation":[{"name":"Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7899-6299","authenticated-orcid":false,"given":"Marco-Christopher","family":"Rupp","sequence":"additional","affiliation":[{"name":"Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Materializing artificial intelligence. 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