{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T20:15:23Z","timestamp":1789676123765,"version":"4.0.1"},"reference-count":30,"publisher":"Elsevier BV","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.acra.2025.07.058","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T11:24:57Z","timestamp":1754997897000},"page":"6905-6912","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multimodal Large Language Models for Pediatric Bone-Age Assessment: A Comparative Accuracy Analysis"],"prefix":"10.1016","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6989-6613","authenticated-orcid":false,"given":"Ra\u015fit Eren","family":"B\u00fcy\u00fcktoka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Salbas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2025.07.058_bib1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000329372","article-title":"The use of bone age in clinical practice\u00a0\u2013 part\u00a01","volume":"76","author":"Martin","year":"2011","journal-title":"Horm Res Paediatr"},{"key":"10.1016\/j.acra.2025.07.058_bib2","doi-asserted-by":"crossref","DOI":"10.1542\/peds.2017-1486","article-title":"Bone age: a handy tool for pediatric providers","volume":"140","author":"Creo","year":"2017","journal-title":"Pediatrics"},{"key":"10.1016\/j.acra.2025.07.058_bib3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1297\/cpe.24.143","article-title":"Bone age: assessment methods and clinical applications","volume":"24","author":"Satoh","year":"2015","journal-title":"Clin Pediatr Endocrinol"},{"key":"10.1016\/j.acra.2025.07.058_bib4","doi-asserted-by":"crossref","DOI":"10.3389\/fped.2021.580314","article-title":"Evaluation of bone age in children: a mini-review","volume":"9","author":"Cavallo","year":"2021","journal-title":"Front Pediatr"},{"key":"10.1016\/j.acra.2025.07.058_bib5","series-title":"Assessment of skeletal maturity and prediction of adult height: TW3 method","author":"Tanner","year":"2001"},{"key":"10.1016\/j.acra.2025.07.058_bib6","series-title":"Radiographic Atlas of skeletal development of the hand and wrist","author":"Greulich","year":"1959"},{"key":"10.1016\/j.acra.2025.07.058_bib7","doi-asserted-by":"crossref","first-page":"507","DOI":"10.2214\/ajr.176.2.1760507","article-title":"Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards","volume":"176","author":"Berst","year":"2001","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2025.07.058_bib8","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1186\/s12887-020-02383-4","article-title":"Reproducibility and inter-observer agreement of Greulich-Pyle protocol to estimate skeletal age among female adolescent soccer players","volume":"20","author":"Faustino-da-Silva","year":"2020","journal-title":"BMC Pediatr"},{"key":"10.1016\/j.acra.2025.07.058_bib9","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.3348\/kjr.2020.1468","article-title":"Clinical validation of a deep learning-based hybrid (Greulich-Pyle and modified Tanner-Whitehouse) method for bone age assessment","volume":"22","author":"Lee","year":"2021","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib10","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1007\/s00247-022-05295-w","article-title":"Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment","volume":"52","author":"Thodberg","year":"2022","journal-title":"Pediatr Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib11","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1007\/s00330-023-10543-0","article-title":"Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method","volume":"34","author":"Gr\u00e4fe","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib12","doi-asserted-by":"crossref","DOI":"10.1007\/s00330-024-11169-6","article-title":"A critical comparative study of the performance of three AI-assisted programs for bone age determination","author":"Pape","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/hcs2.61","article-title":"Large language models in health care: development, applications, and challenges","volume":"2","author":"Yang","year":"2023","journal-title":"Health Care Sci"},{"key":"10.1016\/j.acra.2025.07.058_bib14","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4274\/dir.2023.232417","article-title":"Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions","volume":"30","author":"Akinci\u00a0D\u2032Antonoli","year":"2024","journal-title":"Diagn Interv Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib15","doi-asserted-by":"crossref","first-page":"224","DOI":"10.3348\/kjr.2023.0818","article-title":"Updated primer on generative artificial intelligence and large language models in medical imaging for medical professionals","volume":"25","author":"Kim","year":"2024","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib16","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.232756","article-title":"Chatbots and large language models in radiology: a practical primer for clinical and research applications","volume":"310","author":"Bhayana","year":"2024","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.07.058_bib17","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.3390\/jcm14103282","article-title":"Comparative evaluation of large language and multimodal models in detecting spinal stabilization systems on X-ray images","volume":"14","author":"Polis","year":"2025","journal-title":"J Clin Med"},{"key":"10.1016\/j.acra.2025.07.058_bib18","doi-asserted-by":"crossref","first-page":"3608","DOI":"10.1016\/j.acra.2025.01.033","article-title":"ChatGPT-4o's performance in brain tumor diagnosis and MRI findings: a comparative analysis with radiologists","volume":"32","author":"Ozenbas","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib19","first-page":"196","article-title":"Evaluating Microsoft Bing with ChatGPT-4 for the assessment of abdominal computed tomography and magnetic resonance images","volume":"31","author":"Elek","year":"2025","journal-title":"Diagn Interv Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib20","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.3390\/diagnostics15121513","article-title":"Can AI-based ChatGPT models accurately analyze hand\u2013wrist radiographs? A comparative study","volume":"15","author":"Y\u0131ld\u0131r\u0131m","year":"2025","journal-title":"Diagnostics"},{"key":"10.1016\/j.acra.2025.07.058_bib21","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1148\/radiol.2018180736","article-title":"The RSNA pediatric bone age machine learning challenge","volume":"290","author":"Halabi","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.07.058_bib22","unstructured":"Radiological Society of North America. RSNA Pediatric Bone Age Challenge (2017). Available at: https:\/\/www.rsna.org\/rsnai\/ai-image-challenge\/rsna-pediatric-bone-age-challenge-2017. Accessed July 5, 2025."},{"key":"10.1016\/j.acra.2025.07.058_bib23","unstructured":"Anil R, Borgeaud S, Alayrac J-B, et al. Gemini: A Family of Highly Capable Multimodal Models [preprint]. arXiv 2025; arXiv:2312.11805. Available at: http:\/\/arxiv.org\/abs\/2312.11805. Accessed July 20, 2025."},{"key":"10.1016\/j.acra.2025.07.058_bib24","unstructured":"OpenAI. OpenAI GPT\u20114.5 System Card [Internet]; 2025. Available at: \u3008https:\/\/cdn.openai.com\/gpt-4\u20135-system-card-2272025.pdf\u3009. Accessed July\u00a024,\u00a02025."},{"key":"10.1016\/j.acra.2025.07.058_bib25","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2019190015","article-title":"The effect of image resolution on deep learning in radiography","volume":"2","author":"Sabottke","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2025.07.058_bib26","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1148\/radiol.2017170236","article-title":"Performance of a deep learning neural network model in assessing skeletal maturity on pediatric hand radiographs","volume":"287","author":"Larson","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.07.058_bib27","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.220505","article-title":"Generalizability and bias in a deep learning pediatric bone age prediction model using hand radiographs","volume":"306","author":"Beheshtian","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.07.058_bib28","doi-asserted-by":"crossref","DOI":"10.4274\/dir.2025.242999","article-title":"Automatic bone age assessment: a Turkish population study","author":"\u00d6zt\u00fcrk","year":"2025","journal-title":"Diagn Interv Radiol"},{"key":"10.1016\/j.acra.2025.07.058_bib29","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10354-021-00828-8","article-title":"Epiphysiodesis for the treatment of tall stature and leg length discrepancy","volume":"171","author":"Willegger","year":"2021","journal-title":"Wien Med Wochenschr"},{"key":"10.1016\/j.acra.2025.07.058_bib30","unstructured":"Liu\u00a0H., Li\u00a0C., Wu\u00a0Q., et al. Visual instruction tuning. arXiv. Preprint posted online April\u00a017,\u00a02023. doi:10.48550\/arXiv.2304.08485."}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225007287?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225007287?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T21:00:23Z","timestamp":1763326823000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633225007287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":30,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["S1076633225007287"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2025.07.058","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multimodal Large Language Models for Pediatric Bone-Age Assessment: A Comparative Accuracy Analysis","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2025.07.058","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}