Automatic structuring of radiology reports with on-premise open-source large language models
Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists’ reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-t...
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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2025
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| In: |
European radiology
Year: 2025, Jahrgang: 35, Heft: 4, Pages: 2018-2029 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-11074-y |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1007/s00330-024-11074-y Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00330-024-11074-y 
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| Verfasserangaben: | Piotr Woźnicki, Caroline Laqua, Ina Fiku, Amar Hekalo, Daniel Truhn, Sandy Engelhardt, Jakob Kather, Sebastian Foersch, Tugba Akinci D’Antonoli, Daniel Pinto dos Santos, Bettina Baeßler and Fabian Christopher Laqua |
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| 520 | |a Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists’ reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports. | ||
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