Application of a general LLM-based classification system to retrieve information about oncological trials
The LLM-based classification framework exhibits robust accuracy and adaptability across various oncological trial classification tasks. While there remain some challenges such as strong prompt dependence and high computational and hardware demands, LLMs will play a crucial role in automating the classification of oncological trials and literature as the technology continues to advance.
Authors
- Paul Martin Putora (ORCID: https://orcid.org/0000-0002-4972-6115)
- Stefanie Aeppli (ORCID: https://orcid.org/0000-0003-2779-5942)
- Nikola Čihorić (ORCID: https://orcid.org/0000-0003-1534-8520)
- Fabio Dennstädt (ORCID: https://orcid.org/0000-0002-5374-8720)
- Johannes Zink (ORCID: https://orcid.org/0000-0002-7398-718X)
- Janna Hastings (ORCID: https://orcid.org/0000-0002-3469-4923)
- Mohamed Shelan (ORCID: https://orcid.org/0000-0002-6477-6655)
- Paul Windisch (ORCID: https://orcid.org/0000-0003-1040-4888)
- Marie Wosny (ORCID: https://orcid.org/0009-0000-2791-0774)
- Ahmed Shaheen
- Roberto Gaio
- Max Schmerder (ORCID: https://orcid.org/0009-0009-3663-4412)
- Irina Filchenko (ORCID: https://orcid.org/0000-0002-9486-1017)
Institutions
- University of Bern (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- University of Zurich (CH)
- University of St.Gallen (CH)
- University Hospital of Bern (CH)
- Kantonsspital Winterthur (CH)
- University of Applied Sciences St. Gallen (CH)
- Technical University of Munich (DE)
- Alexandria University (EG)
Publication Details
- Journal
- Oncology
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1159/000546946
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 1.61