In-context learning enables large language models to achieve human-level performance in spinal instability neoplastic score classification from synthetic CT and MRI reports
In-context learning enables state-of-the-art large language models to perform at near-human expert levels in SINS classification, offering potential for automating vertebral metastasis stability assessment. The poor performance of generic large language models highlights the importance of task-specific refinement in medical applications of artificial intelligence.
Authors
- Julia M. Nakagawa
- Horst Urbach (ORCID: https://orcid.org/0000-0001-7264-4807)
- Carl G. Simon (ORCID: https://orcid.org/0000-0001-5209-3577)
- Caroline Wilpert (ORCID: https://orcid.org/0000-0003-2375-2478)
- Marc Hohenhaus (ORCID: https://orcid.org/0000-0002-8743-3161)
- Maximilian Frederik Russe (ORCID: https://orcid.org/0000-0003-3187-2429)
- Marco Reisert (ORCID: https://orcid.org/0000-0003-2742-1940)
- Anna Maria Fink (ORCID: https://orcid.org/0009-0002-9903-283X)
- Alexander Rau (ORCID: https://orcid.org/0000-0001-5881-6043)
- Elmar Kotter (ORCID: https://orcid.org/0000-0001-9022-6000)
Institutions
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
Publication Details
- Journal
- La radiologia medica
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1007/s11547-025-02096-7
- Citations
- 1
- Primary Topic
- Management of metastatic bone disease
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Albert-Ludwigs-Universität Freiburg
- Medizinische Fakultät der Albert-Ludwigs-Universität Freiburg