Enhancing radiology workflows through collaborative AI-assisted chest X-ray reporting using large vision-language models: a proof-of-concept study.
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| Title: | Enhancing radiology workflows through collaborative AI-assisted chest X-ray reporting using large vision-language models: a proof-of-concept study. |
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| Authors: | Pellegrini C; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. chantal.pellegrini@gmail.com.; Munich Center of Machine Learning, Technical University of Munich, Munich, Germany. chantal.pellegrini@gmail.com., Özsoy E; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.; Munich Center of Machine Learning, Technical University of Munich, Munich, Germany., Gassert FT; Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany., Marka AW; Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany., Strenzke M; Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany., Keicher M; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.; Munich Center of Machine Learning, Technical University of Munich, Munich, Germany., Makowski MR; Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany., Navab N; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.; Munich Center of Machine Learning, Technical University of Munich, Munich, Germany. |
| Source: | Insights into imaging [Insights Imaging] 2026 Apr 28; Vol. 17 (1). Date of Electronic Publication: 2026 Apr 28. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Country of Publication: Germany NLM ID: 101532453 Publication Model: Electronic Cited Medium: Print ISSN: 1869-4101 (Print) Linking ISSN: 18694101 NLM ISO Abbreviation: Insights Imaging Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1869-4101 |
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| DOI: | 10.1186/s13244-026-02292-7 |
