Leveraging large language models for accurate classification of liver lesions from MRI reports
These findings highlight the feasibility of LLMs for text-based diagnostic support, particularly in resource-limited or high-volume clinical settings. While LLMs show promise in medical diagnostics, further validation through prospective studies is necessary to ensure reliable clinical integration. The study emphasizes the importance of rigorous benchmarking to assess model performance comprehensively.
Authors
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Friederike Jungmann (ORCID: https://orcid.org/0000-0002-9600-4094)
- Felix Busch (ORCID: https://orcid.org/0000-0001-9770-8555)
- Ulrike Bauer (ORCID: https://orcid.org/0000-0003-2838-2840)
- Florian T. Gassert (ORCID: https://orcid.org/0000-0003-4126-462X)
- Marcus R. Makowski (ORCID: https://orcid.org/0000-0001-8778-647X)
- Daniel Spitzl
- Markus Mergen
Institutions
- TUM Klinikum (DE)
- Deutsches Herzzentrum München (DE)
- Technical University of Munich (DE)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.05.019
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.52