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How green are large language models for radiology report labelling?: comparing human, rule-based and hybrid workflows

To address limited quantitative data on sustainable use of large language models (LLMs) in radiology, we quantified the resource footprint of LLMs for labelling CT pulmonary embolism reports and assessed how a hybrid rule-based-LLM workflow changes time, cost and carbon emissions compared with manua...

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Autori principali: Fink, Matthias A. (Autore) , Bischoff, Arved (Autore) , Atsiatorme, Edem (Autore) , Kremer, Alexander (Autore) , Kroschke, Jonas (Autore) , Moll, Martin (Autore) , Stein, Patrick (Autore) , Riebl, Veronika (Autore) , Leichenich, Timo (Autore) , Kauczor, Hans-Ulrich (Autore) , Schlamp, Kai (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 27 May 2026
In: Insights into imaging
Year: 2026, Volume: 17, Fascicolo: 1, Pages: 1-11
ISSN:1869-4101
DOI:10.1186/s13244-026-02289-2
Accesso online:Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1186/s13244-026-02289-2
Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s13244-026-02289-2
Testo
Note sull'autore:Matthias A. Fink, Arved Bischoff, Edem Atsiatorme, Alexander Kremer, Jonas Kroschke, Martin Moll, Patrick Stein, Veronika Riebl, Timo Leichenich, Hans-Ulrich Kauczor and Kai Schlamp
Descrizione
Riassunto:To address limited quantitative data on sustainable use of large language models (LLMs) in radiology, we quantified the resource footprint of LLMs for labelling CT pulmonary embolism reports and assessed how a hybrid rule-based-LLM workflow changes time, cost and carbon emissions compared with manual labelling.
Descrizione del documento:Gesehen am 26.08.2026
Descrizione fisica:Online Resource
ISSN:1869-4101
DOI:10.1186/s13244-026-02289-2

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