Evaluating diagnostic accuracy of large language models in neuroradiology cases using image inputs from JAMA neurology and JAMA clinical challenges.

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Title: Evaluating diagnostic accuracy of large language models in neuroradiology cases using image inputs from JAMA neurology and JAMA clinical challenges.
Authors: Albaqshi A; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Ko JS; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Suh CH; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. chonghyunsuh@amc.seoul.kr., Suh PS; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea., Shim WH; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.; Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea., Heo H; Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea., Woo CY; Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea., Park H; Department of Pulmonology, Shihwa Medical Center, Siheung, Republic of Korea.
Source: Scientific reports [Sci Rep] 2025 Nov 27; Vol. 15 (1), pp. 43027. Date of Electronic Publication: 2025 Nov 27.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-025-06458-z