Comprehensive testing of large language models for extraction of structured data in pathology
Open-source language models demonstrate comparable performance to proprietary solutions in structuring pathology report data. This finding has significant implications for healthcare institutions seeking cost-effective, privacy-preserving data structuring solutions. The variations in model performance across different configurations provide valuable insights for practical deployment in pathology departments. Our publicly available bilingual dataset serves as both a benchmark and a resource for future research.
Authors
- Reinhard Büttner (ORCID: https://orcid.org/0000-0001-8806-4786)
- Alexander Quaas (ORCID: https://orcid.org/0000-0002-9644-2115)
- Birgid Schömig‐Markiefka (ORCID: https://orcid.org/0000-0003-1893-8796)
- Yuri Tolkach (ORCID: https://orcid.org/0000-0001-5239-2841)
- Alen Brkic (ORCID: https://orcid.org/0000-0002-6371-8947)
- Bastian Grothey (ORCID: https://orcid.org/0000-0002-0883-6481)
- Jan Odenkirchen
Institutions
- University of Cologne (DE)
- University Hospital Cologne (DE)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1038/s43856-025-00808-8
- Citations
- 22
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 54.58
Funders
- Wilhelm Sander-Stiftung
- Bundesministerium für Bildung und Forschung