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Uncover This Tech Term: Large Vision-Language Models in Radiology

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dc.contributor.authorFaghani, Shahriar-
dc.contributor.authorPark, Yae Won-
dc.contributor.authorPark, Ji Eun-
dc.contributor.author박예원-
dc.date.accessioned2026-04-07T02:08:20Z-
dc.date.available2026-04-07T02:08:20Z-
dc.date.created2026-04-01-
dc.date.issued2026-04-
dc.identifier.issn1229-6929-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/211787-
dc.description.abstractLarge multimodal models are typically transformer-based foundational models that can process and generate multiple types of data (modalities), including text, images, audio, and video [1,2]. Large vision-language models (LVLMs) are a subset of large multimodal models that specifically focus on aligning and integrating visual and linguistic systems are trained to perform well-defined narrow tasks and have limited adaptability. By contrast, LVLMs generalize across diverse tasks and support flexible downstream applications without requiring task-specific retraining.-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherKorean Society of Radiology-
dc.relation.isPartOfKOREAN JOURNAL OF RADIOLOGY-
dc.relation.isPartOfKOREAN JOURNAL OF RADIOLOGY-
dc.titleUncover This Tech Term: Large Vision-Language Models in Radiology-
dc.typeArticle-
dc.contributor.googleauthorFaghani, Shahriar-
dc.contributor.googleauthorPark, Yae Won-
dc.contributor.googleauthorPark, Ji Eun-
dc.identifier.doi10.3348/kjr.2025.1813-
dc.relation.journalcodeJ02884-
dc.identifier.eissn2005-8330-
dc.identifier.pmid41776854-
dc.subject.keywordLarge vision-language model-
dc.subject.keywordVision-language model-
dc.subject.keywordLarge multimodal model-
dc.subject.keywordLarge language model-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordTransformer-
dc.contributor.affiliatedAuthorPark, Yae Won-
dc.identifier.wosid001724504800009-
dc.citation.volume27-
dc.citation.number4-
dc.citation.startPage375-
dc.citation.endPage378-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF RADIOLOGY, Vol.27(4) : 375-378, 2026-04-
dc.identifier.rimsid92270-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorLarge vision-language model-
dc.subject.keywordAuthorVision-language model-
dc.subject.keywordAuthorLarge multimodal model-
dc.subject.keywordAuthorLarge language model-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorTransformer-
dc.type.docTypeArticle-
dc.identifier.kciidART003315019-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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