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Prospective evaluation of artificial intelligence (AI) in lumbar spine magnetic resonance imaging (MRI) workflow: from deep learning (DL)-enhanced accelerated acquisition to simultaneous vision-language model (VLM)-based automated report generation.

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Title: Prospective evaluation of artificial intelligence (AI) in lumbar spine magnetic resonance imaging (MRI) workflow: from deep learning (DL)-enhanced accelerated acquisition to simultaneous vision-language model (VLM)-based automated report generation.
Authors: Park J; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea., Han K; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea., Oh JS; Department of Radiology, Seoul National University Hospital, Seoul, South Korea., Chae HD; Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea., Kim A; Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, South Korea., Park SY; Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, South Korea., Yoo HJ; Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea., Lee YH; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea. Electronic address: sando@yuhs.ac.
Source: European journal of radiology [Eur J Radiol] 2026 Mar; Vol. 196, pp. 112695. Date of Electronic Publication: 2026 Jan 21.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 8106411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7727 (Electronic) Linking ISSN: 0720048X NLM ISO Abbreviation: Eur J Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Prospective evaluation of artificial intelligence (AI) in lumbar spine magnetic resonance imaging (MRI) workflow: from deep learning (DL)-enhanced accelerated acquisition to simultaneous vision-language model (VLM)-based automated report generation.
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  Data: <searchLink fieldCode="AU" term="%22Park+J%22">Park J</searchLink>; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Han+K%22">Han K</searchLink>; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Oh+JS%22">Oh JS</searchLink>; Department of Radiology, Seoul National University Hospital, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Chae+HD%22">Chae HD</searchLink>; Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Kim+A%22">Kim A</searchLink>; Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Park+SY%22">Park SY</searchLink>; Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Yoo+HJ%22">Yoo HJ</searchLink>; Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea.<br /><searchLink fieldCode="AU" term="%22Lee+YH%22">Lee YH</searchLink>; Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea. Electronic address: sando@yuhs.ac.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Science+Ireland+Ltd%22">Elsevier Science Ireland Ltd </searchLink><i>Country of Publication: </i>Ireland <i>NLM ID: </i>8106411 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1872-7727 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220720048X%22">0720048X </searchLink><i>NLM ISO Abbreviation: </i>Eur J Radiol <i>Subsets: </i>MEDLINE
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              Text: 2026 Mar
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