{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T08:46:52Z","timestamp":1791794812724,"version":"4.3.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["P0056738"],"award-info":[{"award-number":["P0056738"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) provides a standardized framework for thyroid nodule risk stratification based on predefined ultrasound features. This study aimed to evaluate the accuracy and robustness of ChatGPT-5.6 Sol in extracting and scoring ACR TI-RADS features and classifying thyroid nodules from synthetic free-text ultrasound reports.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A primary dataset of 100 synthetic thyroid ultrasound reports was constructed, with each report describing 1 nodule and the 5 ACR TI-RADS feature categories. ChatGPT-5.6 Sol was evaluated using a structured prompting strategy for feature identification, feature scoring, total score calculation, and final Thyroid Imaging Reporting and Data System classification. A less-directive prompt was also tested for total score calculation and final classification. Robustness was further assessed using a 30-case synthetic challenge set balanced across TR1 to TR5. Outputs were compared with reference annotations based on the 2017 ACR TI-RADS criteria. Accuracy was reported with Wilson 95% confidence intervals, and final-category agreement was assessed using Cohen's kappa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the primary dataset, accuracy for both feature identification and scoring was 100.0% for each ACR TI-RADS feature category (100\/100; 95% confidence interval: 96.3%\u2013100.0%). Total score calculation and final classification were also correct in all cases (100\/100; 95% confidence interval: 96.3%\u2013100.0%), with a Cohen's \u03ba of 1.000. In the challenge set, feature identification, feature scoring, total score calculation, and final classification were correct in all 30 cases (30\/30; 95% confidence interval: 88.6%\u2013100.0%), with a Cohen's \u03ba of 1.000. The less-directive prompt produced identical total score and classification accuracy in both datasets, with no discordant cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ChatGPT-5.6 Sol demonstrated high accuracy and consistent ACR TI-RADS\u2013based classification across both synthetic datasets. Large language models may have potential for standardized Thyroid Imaging Reporting and Data System interpretation of free-text ultrasound reports; however, further validation in larger and more diverse settings is needed to establish generalizability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605261494875","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T07:19:54Z","timestamp":1791530394000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the accuracy of ChatGPT-5.6 in American College of Radiology Thyroid Imaging Reporting and Data System feature scoring and classification from synthetic free-text thyroid ultrasound reports"],"prefix":"10.1177","volume":"54","author":[{"given":"Ziman","family":"Chen","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingli","family":"Wang","sequence":"additional","affiliation":[{"name":"Ultrasound Department, EDAN Instruments, Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2362.2009.02162.x"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.31128\/AJGP-03-18-4518"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2017.01.046"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-024-05125-7"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.5435\/JAAOS-D-24-01049"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-025-11484-6"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01468-7"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjps.2025.04.025"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s42492-025-00189-8"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-024-00526-1"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15050635"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111458"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjoto.2023.104144"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15202617"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-024-04066-x"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eprac.2025.03.008"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjoto.2025.104696"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12020-026-04622-7"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-026-55656-w"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics16020313"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eehl.2025.100156"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinsp.2026.100967"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpsurg.2026.101999"}],"container-title":["Journal of International Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/03000605261494875","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/03000605261494875","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/03000605261494875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T08:16:35Z","timestamp":1791792995000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/03000605261494875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":23,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1177\/03000605261494875"],"URL":"https:\/\/doi.org\/10.1177\/03000605261494875","relation":{},"ISSN":["0300-0605","1473-2300"],"issn-type":[{"value":"0300-0605","type":"print"},{"value":"1473-2300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]},"article-number":"03000605261494875"}}