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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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Main Authors: Woźnicki, Piotr (Author) , Laqua, Caroline (Author) , Fiku, Ina (Author) , Hekalo, Amar (Author) , Truhn, Daniel (Author) , Engelhardt, Sandy (Author) , Kather, Jakob Nikolas (Author) , Försch, Sebastian (Author) , D’Antonoli, Tugba Akinci (Author) , Pinto dos Santos, Daniel (Author) , Baeßler, Bettina (Author) , Laqua, Fabian Christopher (Author)
Format: Article (Journal)
Language:English
Published: 2025
In: European radiology
Year: 2025, Volume: 35, Issue: 4, Pages: 2018-2029
ISSN:1432-1084
DOI:10.1007/s00330-024-11074-y
Online Access: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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Author Notes: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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Summary: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.
Item Description:Online veröffentlicht: 10. Oktober 2024
Gesehen am 03.04.2025
Physical Description:Online Resource
ISSN:1432-1084
DOI:10.1007/s00330-024-11074-y

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