{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T12:13:28Z","timestamp":1788178408196,"version":"build-2803163510"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"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":[[2026,8]]},"DOI":"10.1016\/j.acra.2026.08.077","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T11:13:50Z","timestamp":1788174830000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance of Frontier Large Language Models on an FRCR Part 1-Style Examination: A Comparative Study of Physics and Anatomy Modules"],"prefix":"10.1016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-0840","authenticated-orcid":false,"given":"Hamza Eren","family":"G\u00fczel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ba\u015fak","family":"\u00dcnverdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.acra.2026.08.077_bib1","doi-asserted-by":"crossref","first-page":"7998","DOI":"10.1007\/s00330-022-08784-6","article-title":"Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)","volume":"32","author":"Kelly","year":"2022","journal-title":"Eur Radiol"},{"issue":"17","key":"10.1016\/j.acra.2026.08.077_bib2","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.3390\/diagnostics13172760","article-title":"Redefining radiology: a review of artificial intelligence integration in medical imaging","volume":"13","author":"Najjar","year":"2023","journal-title":"Diagnostics\u00a0(Basel)"},{"issue":"9","key":"10.1016\/j.acra.2026.08.077_bib3","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1177\/02841851241263066","article-title":"A systematic review of deep learning-based spinal bone lesion detection in medical images","volume":"65","author":"Teodorescu","year":"2024","journal-title":"Acta Radiol"},{"key":"10.1016\/j.acra.2026.08.077_bib4","article-title":"Diagnostic precision of a deep learning algorithm for the classification of non-contrast brain CT reports","volume":"5","author":"G\u00fczel","year":"2025","journal-title":"Front Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.08.077_bib5","article-title":"Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe","volume":"5","author":"Walsh","year":"2023","journal-title":"BJR Open"},{"key":"10.1016\/j.acra.2026.08.077_bib6","series-title":"FRCR Part 1 (Radiology) - CR1: guidance notes for candidates","author":"The Royal College of Radiologists","year":"2026"},{"issue":"7","key":"10.1016\/j.acra.2026.08.077_bib7","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s11604-024-01552-0","article-title":"The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI","volume":"42","author":"Nakaura","year":"2024","journal-title":"Jpn J Radiol"},{"issue":"9","key":"10.1016\/j.acra.2026.08.077_bib8","doi-asserted-by":"crossref","first-page":"3872","DOI":"10.1016\/j.acra.2024.06.046","article-title":"Large language models as tools to generate radiology board-style multiple-choice questions","volume":"31","author":"Mistry","year":"2024","journal-title":"Acad Radiol"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib9","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.241191","article-title":"Llama 3 challenges proprietary state-of-the-art large language models in radiology board-style examination questions","volume":"312","author":"Adams","year":"2024","journal-title":"Radiology"},{"issue":"5","key":"10.1016\/j.acra.2026.08.077_bib10","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1016\/j.acra.2026.01.040","article-title":"Large language models solving the European Diploma in Radiology: a comparative evaluation","volume":"33","author":"G\u00fczel","year":"2026","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.08.077_bib11","first-page":"190","article-title":"Assessment of large language models in musculoskeletal radiological anatomy: a comparative study with radiologists","volume":"37","author":"Salbas","year":"2026","journal-title":"Jt Dis Relat Surg"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib12","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.acra.2025.10.043","article-title":"Performance of large language models on radiology residency in-training examination questions","volume":"33","author":"Salbas","year":"2026","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2026.08.077_bib13","series-title":"Farr\u2019s Physics for Medical Imaging","author":"Yucel-Finn","year":"2023"},{"key":"10.1016\/j.acra.2026.08.077_bib14","unstructured":"Radiology Cafe. Mock anatomy exams. Available at: https:\/\/www.radiologycafe.com\/exams\/mock-anatomy-exams\/. Accessed April 15, 2026."},{"issue":"5","key":"10.1016\/j.acra.2026.08.077_bib15","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230582","article-title":"Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations","volume":"307","author":"Bhayana","year":"2023","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib16","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1055\/s-0044-1792040","article-title":"Evaluating ChatGPT-4's performance in identifying radiological anatomy in FRCR Part 1 examination questions","volume":"35","author":"Sarangi","year":"2025","journal-title":"Indian J Radiol Imaging"},{"issue":"10","key":"10.1016\/j.acra.2026.08.077_bib17","first-page":"1057","article-title":"Large language models in radiology exams for medical students: performance and consequences","volume":"197","author":"Gotta","year":"2025","journal-title":"Rofo"},{"issue":"3","key":"10.1016\/j.acra.2026.08.077_bib18","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.240153","article-title":"Performance of GPT-4 with vision on text- and image-based ACR Diagnostic Radiology In-Training Examination questions","volume":"312","author":"Hayden","year":"2024","journal-title":"Radiology"},{"issue":"8","key":"10.1016\/j.acra.2026.08.077_bib19","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1007\/s11604-024-01561-z","article-title":"GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination","volume":"42","author":"Hirano","year":"2024","journal-title":"Jpn J Radiol"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib20","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s11604-025-01861-y","article-title":"Assessing accuracy and legitimacy of multimodal large language models on Japan Diagnostic Radiology Board Examination","volume":"44","author":"Hirano","year":"2026","journal-title":"Jpn J Radiol"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib21","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.acra.2025.10.051","article-title":"Performance of state-of-the-art multimodal large language models on an image-rich radiology board examination: comparison to human examinees","volume":"33","author":"Nakaura","year":"2026","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2026.08.077_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2024.111842","article-title":"Large language models in radiology: fluctuating performance and decreasing discordance over time","volume":"182","author":"Gupta","year":"2025","journal-title":"Eur J Radiol"},{"issue":"10","key":"10.1016\/j.acra.2026.08.077_bib23","doi-asserted-by":"crossref","first-page":"900","DOI":"10.3348\/kjr.2025.0599","article-title":"Multimodal large language models in medical imaging: current state and future directions","volume":"26","author":"Nam","year":"2025","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.acra.2026.08.077_bib24","doi-asserted-by":"crossref","DOI":"10.4274\/dir.2026.263696","article-title":"Large-scale evaluation of multimodal large language models for pneumothorax detection","author":"G\u00fczel","year":"2026","journal-title":"Diagn Interv Radiol"},{"issue":"11","key":"10.1016\/j.acra.2026.08.077_bib25","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.3390\/diagnostics16111602","article-title":"Performance of multimodal large language models in detection and position assessment of thoracic devices on chest radiographs","volume":"16","author":"G\u00fczel","year":"2026","journal-title":"Diagnostics\u00a0(Basel)"},{"issue":"2","key":"10.1016\/j.acra.2026.08.077_bib26","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.250913","article-title":"Guidelines for reporting studies on large language models in radiology: an international Delphi expert survey","volume":"318","author":"Kottlors","year":"2026","journal-title":"Radiology"},{"issue":"5","key":"10.1016\/j.acra.2026.08.077_bib27","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s13534-025-00484-6","article-title":"Vision-language foundation models for medical imaging: a review of current practices and innovations","volume":"15","author":"Ryu","year":"2025","journal-title":"Biomed Eng Lett"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226006896?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226006896?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T11:13:54Z","timestamp":1788174834000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633226006896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":27,"alternative-id":["S1076633226006896"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2026.08.077","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Performance of Frontier Large Language Models on an FRCR Part 1-Style Examination: A Comparative Study of Physics and Anatomy Modules","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2026.08.077","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 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"}]}}