Large Language Models for Simplified Interventional Radiology Reports: A Comparative Analysis
With the increasing complexity of interventional radiology (IR) procedures and the growing availability of electronic health records, simplifying IR reports is critical to improving patient understanding and clinical decision-making. This study provides insights into the performance of various LLMs in rewriting IR reports, which can help in selecting the most suitable model for clinical patient-centered applications.
Authors
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Elif Can (ORCID: https://orcid.org/0000-0001-5319-7570)
- Wibke Uller (ORCID: https://orcid.org/0000-0001-6756-9005)
- Felix Busch (ORCID: https://orcid.org/0000-0001-9770-8555)
- Stephan Ellmann (ORCID: https://orcid.org/0000-0003-2737-5526)
- Marcus R. Makowski (ORCID: https://orcid.org/0000-0001-8778-647X)
- Nadine Bayerl (ORCID: https://orcid.org/0000-0002-8530-6783)
- Avan Kader (ORCID: https://orcid.org/0000-0002-5453-1267)
- Michael Doppler
- Katharina Sophie Vogt (ORCID: https://orcid.org/0000-0003-3911-637X)
- Aboelyazid Elkilany (ORCID: https://orcid.org/0000-0002-4311-0520)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University Medical Center Freiburg (DE)
- TUM Klinikum (DE)
- Universitätsklinikum Erlangen (DE)
- University Hospital Leipzig (DE)
Publication Details
- Journal
- Academic Radiology
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1016/j.acra.2024.09.041
- Citations
- 26
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.51