{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T05:50:12Z","timestamp":1791957012771,"version":"4.3.4"},"reference-count":104,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Orthop Surg (Hong Kong)"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI), especially machine and deep learning, is an emerging tool in medicine with growing applications in orthopedic imaging. Given imaging\u2019s central role in diagnosis, surgical planning, and treatment monitoring, this narrative review maps the current and potential uses of AI in orthopedics, identifies underdevelopment sections, and emphasizes limitations in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A PubMed search used the terms \u201cOrthopedic,\u201d \u201cImaging,\u201d and \u201cArtificial Intelligence.\u201d Studies were screened by title and abstract, excluding non-orthopedic or non-AI studies, animal research, pediatric populations, and robotics or pharmacology papers. Eighty-five peer-reviewed articles were analyzed for imaging modality, clinical application, AI technique, and assigned a level of evidence. They were selected based on relevance to AI in orthopedic imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI applications spanned radiography, CT, MRI, DTI, and ultrasound. For bone pathology, AI aided fracture detection and osteoarthritis prediction with clinician-level accuracy. In soft tissue injuries, models assisted in rotator cuff and meniscus tear detection and muscle segmentation. AI-driven 3D reconstructions and virtual reduction models enhanced surgical planning, particularly in joint replacement and trauma. Spine imaging benefited from vertebral segmentation, disc degeneration classification, and paraspinal muscle analysis, improving preoperative planning and outcome prediction. radiography, CT, MRI, DTI, and ultrasound. An important distinction is that many of these models generated outputs that surrounded conditional probabilities rather than categorical diagnoses. Many pitfalls remain including single-center overfitting, data leakage, and inter-reader variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI demonstrates potential to enhance diagnostic accuracy, streamline surgical planning, and improve workflow efficiency. Challenges remain, including data bias, regulatory constraints, and ethical considerations, warranting caution to its blanket application in medicine. Continued integration and validation are needed for optimal clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>AI may serve as an adjunct to clinicians, supporting imaging interpretation and surgical decision-making while preserving human judgment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10225536261466359","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T05:41:21Z","timestamp":1791956481000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Current applications and challenges of artificial intelligence in orthopedic imaging: A narrative review"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1942-6197","authenticated-orcid":false,"given":"Taylor F.","family":"Faust","sequence":"first","affiliation":[{"name":"Stony Brook University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua L.","family":"Dale","sequence":"additional","affiliation":[{"name":"University of Mississippi Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Matos","sequence":"additional","affiliation":[{"name":"Stony Brook University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quinn T.","family":"Astrachan","sequence":"additional","affiliation":[{"name":"Conway Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan M.","family":"Leverant","sequence":"additional","affiliation":[{"name":"Dartmouth Hitchcock Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grace V.","family":"Gilbert","sequence":"additional","affiliation":[{"name":"Cooper University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Lynch","sequence":"additional","affiliation":[{"name":"Stony Brook University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"issue":"11","key":"e_1_3_4_2_2","first-page":"e97674","article-title":"Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration","volume":"17","author":"Elkohail A","year":"2025","unstructured":"Elkohail A, Soffar A, Paul A, et al. 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