{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:58:27Z","timestamp":1780941507246,"version":"3.54.1"},"reference-count":17,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["arthroscopyjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthroscopy, Sports Medicine, and Rehabil."],"published-print":{"date-parts":[[2026,4]]},"abstract":"\n Purpose<\/jats:title>\n To assess the ability of artificial intelligence (AI) to identify common sports\u2010related pathologies using radiologic imaging and to compare ChatGPT 4.0 with 2 competitors, Grok 2 and Claude 3.5 Sonnet.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet were used. Five common orthopaedic sports pathologies were chosen: anterior cruciate ligament tears, posterior cruciate ligament tears, meniscal tears, chondral pathologies, and rotator cuff tears. Fifty images representing each pathology were collected from a radiologic imaging database when possible, which included radiographic images, computed tomography, and magnetic resonance imaging. Normal images were collected that corresponded to each diagnostic category. Receiver operator characteristic curves and area under the curve values were calculated to assess the accuracy of each AI platform.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet accurately identified the pathology in 23.6%, 15.7%, and 17.1% of diseased images, respectively. ChatGPT and Grok were most accurate at identifying meniscus pathologies (ChatGPT: 48%, Grok: 42%), whereas Claude Sonnet was most accurate at identifying anterior cruciate ligament pathologies (30%). The area under the curve for ChatGPT, Grok, and Claude Sonnet was 0.21, 0.16, and 0.15, respectively (ChatGPT 4.0 vs Grok 2,\n P<\/jats:italic>\n \u2009=\u2009.30; ChatGPT 4.0 vs Claude 3.5 Sonnet,\n P<\/jats:italic>\n \u2009=\u2009.24; Grok 2 vs Claude 3.5 Sonnet,\n P<\/jats:italic>\n \u2009>\u2009.99). There were no differences in performance between the 3 platforms overall or within any of the diagnostic categories.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet correctly identified the pathology in less than 25% of images of common sports\u2010related pathologies and showed area under the curve values well below 0.5, indicating poor accuracy. Based on these findings, we do not recommend the current use of these generative AI models for image\u2010based diagnosis in orthopaedics.<\/jats:p>\n <\/jats:sec>\n \n Clinical Relevance<\/jats:title>\n As the use of AI becomes more popular within the general public, it becomes increasingly important to make aware the capabilities and limitations of popular AI platforms in regard to their current image\u2010based diagnostic capabilities.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/ars2.70009","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T07:15:17Z","timestamp":1777533317000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ChatGPT, Claude Sonnet, and Grok Display Similarly Low Rates of Accuracy in Identifying Image\u2010Based Orthopaedic Sports Pathologies"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0311-1588","authenticated-orcid":false,"given":"Ahmad R.","family":"Alhankawi","sequence":"first","affiliation":[{"name":"Mayo Clinic Alix School of Medicine Scottsdale Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Collin L.","family":"Braithwaite","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Mayo Clinic Arizona Phoenix Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1936-9155","authenticated-orcid":false,"given":"Alejandro M.","family":"Holle","sequence":"additional","affiliation":[{"name":"Mayo Clinic Alix School of Medicine Scottsdale Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3003-3316","authenticated-orcid":false,"given":"M. Lane","family":"Moore","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Mayo Clinic Arizona Phoenix Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saad","family":"Tarabichi","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Mayo Clinic Arizona Phoenix Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4264-079X","authenticated-orcid":false,"given":"Anikar","family":"Chhabra","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Mayo Clinic Arizona Phoenix Arizona U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/1062860617751639"},{"key":"e_1_2_7_3_1","first-page":"185","article-title":"Comprehensiveness, accuracy, quality, credibility and readability of online information about knee osteoarthritis","volume":"52","author":"Goff AJ","year":"2023","journal-title":"Health Inf Manag."},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asmr.2021.05.007"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2017.09.043"},{"key":"e_1_2_7_6_1","unstructured":"McClainC SidotiO.34% of U.S. adults have used ChatGPT about double the share in 2023.Pew Research Center2024.https:\/\/www.pewresearch.org\/short-reads\/2025\/06\/25\/34-of-us-adults-have-used-chatgpt-about-double-the-share-in-2023\/. Accessed March 14 2025."},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11212906"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0307383"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.2196\/48659"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-58760-x"},{"key":"e_1_2_7_11_1","first-page":"e41435","article-title":"ChatGPT in radiology: The advantages and limitations of artificial intelligence for medical imaging diagnosis","volume":"15","author":"Srivastav S","year":"2023","journal-title":"Cureus."},{"key":"e_1_2_7_12_1","first-page":"155894472412989","article-title":"Is artificial intelligence the future of radiology? Accuracy of ChatGPT in radiologic diagnosis of upper extremity bony pathology","author":"Hiredesai AN","year":"2024","journal-title":"Hand (N Y)."},{"key":"e_1_2_7_13_1","unstructured":"Radiopaedia.Radiopaedia.org the wiki\u2010based collaborative Radiology resource.2019.https:\/\/radiopaedia.org\/. Accessed June 10 2025."},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.2196\/55627"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2020.09.012"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00581-3"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00402-023-05113-4"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10213-1"}],"container-title":["Arthroscopy, Sports Medicine, and Rehabilitation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/arthroscopyjournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ars2.70009","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/arthroscopyjournals.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ars2.70009","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/arthroscopyjournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ars2.70009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:12:07Z","timestamp":1780938727000},"score":1,"resource":{"primary":{"URL":"https:\/\/arthroscopyjournals.onlinelibrary.wiley.com\/doi\/10.1002\/ars2.70009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1002\/ars2.70009"],"URL":"https:\/\/doi.org\/10.1002\/ars2.70009","archive":["Portico"],"relation":{},"ISSN":["2666-061X","2666-061X"],"issn-type":[{"value":"2666-061X","type":"print"},{"value":"2666-061X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"2025-07-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70009"}}