{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T22:03:03Z","timestamp":1793656983237,"version":"4.5.1"},"reference-count":14,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"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":["Hand (New York, N,Y.)"],"published-print":{"date-parts":[[2025,10]]},"abstract":"\n Background:<\/jats:title>\n The purpose of this study was to assess the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of hand and wrist radiology reports.<\/jats:p>\n <\/jats:sec>\n \n Methods:<\/jats:title>\n The radiology reports of 100 hand and\/or wrist radiographs, 100 hand and\/or wrist computed tomography (CT) scans, and 100 hand and\/or wrist magnetic resonance imaging (MRI) scans were extracted. The following prompt command was inserted into the AI-LLM: \u201cExplain this radiology report to a patient in layman\u2019s terms in the second person: [Report Text].\u201d The report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for the original radiology report and the AI-LLM\u2013generated report. The accuracy of the AI-LLM report was assessed via a 5-point Likert scale. Any \u201challucination\u201d produced by the AI-LLM\u2013generated report was recorded.<\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n There was a statistically significant improvement in mean FRES scores and FKRL scores in the AI-LLM\u2013generated radiograph report, CT report, and MRI report. For all AI-LLM\u2013generated reports, the mean reading level improved to below an eighth-grade reading level. The mean Likert score for the AI-LLM\u2013generated radiograph report, CT report, and MRI report was 4.1 \u00b1 0.6, 3.9 \u00b1 0.6, and 3.9 \u00b1 0.7, respectively. The hallucination rate in the AI-LLM\u2013generated radiograph report, CT report, and MRI report was 3%, 6%, and 6%, respectively.<\/jats:p>\n <\/jats:sec>\n \n Conclusions:<\/jats:title>\n This study demonstrates that AI-LLM effectively improves the readability of hand and wrist radiology reports, underscoring the potential application of AI-LLM as a promising and innovative patient-centric strategy to improve patient comprehension of their imaging reports.<\/jats:p>\n \n Level of Evidence:<\/jats:bold>\n IV.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1177\/15589447241267766","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T01:08:26Z","timestamp":1723597706000},"page":"1144-1152","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["Decoding Radiology Reports: Artificial Intelligence-Large Language Models Can Improve the Readability of Hand and Wrist Orthopedic Radiology Reports"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4212-5018","authenticated-orcid":false,"given":"James J.","family":"Butler","sequence":"first","affiliation":[{"name":"NYU Langone Health, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto","family":"Acosta","sequence":"additional","affiliation":[{"name":"Albany Medical Center, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Kuna","sequence":"additional","affiliation":[{"name":"Albany Medical Center, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Harrington","sequence":"additional","affiliation":[{"name":"Albany Medical Center, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew J.","family":"Rosenbaum","sequence":"additional","affiliation":[{"name":"Albany Medical Center, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2457-2962","authenticated-orcid":false,"given":"Michael T.","family":"Mulligan","sequence":"additional","affiliation":[{"name":"Albany Medical Center, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John G.","family":"Kennedy","sequence":"additional","affiliation":[{"name":"NYU Langone Health, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.221263"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2017.03.005"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/1742271X221140024"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.22.27512"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2023.02.003"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0057532"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Kincaid J Fishburne R Rogers R Chissom B. Derivation of new readability formulas (automated readability index fog count and Flesch reading ease formula) for navy enlisted personnel. Institute for Simulation and Training. 1975. https:\/\/stars.library.ucf.edu\/istlibrary\/56","DOI":"10.21236\/ADA006655"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.35179"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2018.12.006"},{"key":"e_1_3_2_11_2","first-page":"399","article-title":"Towards consumer-friendly PHRs: patients\u2019 experience with reviewing their health records","volume":"2007","author":"Keselman A","year":"2007","unstructured":"Keselman A, Slaughter L, Smith CA, et al. Towards consumer-friendly PHRs: patients\u2019 experience with reviewing their health records. AMIA Annu Symp Proc. 2007;2007:399-403.","journal-title":"AMIA Annu Symp Proc"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy065"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1067\/j.cpradiol.2022.01.012"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.22549"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1177\/2374373521998847"}],"container-title":["HAND"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15589447241267766","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15589447241267766","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15589447241267766","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:29:37Z","timestamp":1777447777000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15589447241267766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":14,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1177\/15589447241267766"],"URL":"https:\/\/doi.org\/10.1177\/15589447241267766","relation":{},"ISSN":["1558-9447","1558-9455"],"issn-type":[{"value":"1558-9447","type":"print"},{"value":"1558-9455","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]}}}