LLMPaper

Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Great study to systematically benchmark various LLMs and prompting strategies to extract structured information from radiological and pathological reports across six use cases and multiple languages. These findings show that open-weight LLMs can extract structured data from clinical reports across diseases, languages, and institutions, offering a scalable approach for clinical data curation.
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina, Kaouther Mouheb, Lukáš Hejtmánek, Matthew Marzetti, Antonius W. Schurink, Damian Chan, Ruben Niemantsverdriet, Frederik Hartmann, Zhen Qian, Maarten G. J. Thomeer, Petr Holub, Farhan Akram, Frank J. Wolters, Meike W. Vernooij, Cornelis Verhoef, Esther E. Bron, Vít Nováček, Dirk J. Grünhagen, Wiro J. Niessen, Martijn P. A. Starmans, Stefan Klein
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages