{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T23:26:27Z","timestamp":1787786787662,"version":"build-2784847793"},"reference-count":76,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Early and accurate diagnosis is critical to preserve function and reduce healthcare costs in patients with hand and wrist injury. As such, artificial intelligence (AI) models have been developed for the purpose of diagnosing fractures through imaging. The purpose of this systematic review and meta-analysis was to determine the accuracy of AI models in identifying hand and wrist fractures and dislocations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Diagnostic Test Accuracy guidelines, Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched from their inception to October 10, 2023. Studies were included if they utilized an AI model (index test) for detecting hand and wrist fractures and dislocations in pediatric (&lt;18 years) or adult (&gt;18 years) patients through any radiologic imaging, with the reference standard established through image review by a medical expert. Results were synthesized through bivariate analysis. Risk of bias was assessed using the QUADAS-2 tool. This study was registered with PROSPERO (CRD42023486475). Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>A systematic review identified 36 studies. Most studies assessed wrist fractures (27.90%) through radiograph imaging (94.44%), with radiologists serving as the reference standard (66.67%). AI models demonstrated area under the curve (0.946), positive likelihood ratio (7.690; 95% confidence interval, 6.400-9.190), and negative likelihood ratio (0.112; 0.0848-0.145) in diagnosing hand and wrist fractures and dislocations. Examining only studies characterized by a low risk of bias, sensitivity analysis did not reveal any difference from the overall results. Overall certainty of evidence was moderate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>In demonstrating the accuracy of AI models in hand and wrist fracture and dislocation diagnosis, we have demonstrated that the potential use of AI in diagnosing hand and wrist fractures is promising.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence:<\/jats:title>\n                    <jats:p>\n                      <jats:underline>Level III<\/jats:underline>\n                      . See Instructions for Authors for a complete description of levels of evidence.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.2106\/jbjs.rvw.24.00106","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T14:53:25Z","timestamp":1725548005000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":11,"title":["The Accuracy of Artificial Intelligence Models in Hand\/Wrist Fracture and Dislocation Diagnosis"],"prefix":"10.2106","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3595-5160","authenticated-orcid":false,"given":"Chloe R.","family":"Wong","sequence":"first","affiliation":[{"name":"Division of Plastic, Reconstructive & Aesthetic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5387-5917","authenticated-orcid":false,"given":"Alice","family":"Zhu","sequence":"additional","affiliation":[{"name":"Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9185-206X","authenticated-orcid":false,"given":"Heather L.","family":"Baltzer","sequence":"additional","affiliation":[{"name":"Division of Plastic, Reconstructive & Aesthetic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2024,9]]},"reference":[{"issue":"1","key":"r1-20240905","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"issue":"11","key":"r2-20240905","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.1097\/CORR.0000000000000848","article-title":"What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? 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