Performance of open-source and proprietary large language models in generating patient-friendly radiology chest CT reports
Llama-3-70b shows strong potential in generating quality, patient-friendly radiology reports, challenging proprietary models. With further adaptation, open-source LLMs could advance patient-friendly reporting technology.
Authors
- Christian Mertens (ORCID: https://orcid.org/0000-0001-7303-2650)
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Felix Busch (ORCID: https://orcid.org/0000-0001-9770-8555)
- Marcus R. Makowski (ORCID: https://orcid.org/0000-0001-8778-647X)
- Nadine Bayerl (ORCID: https://orcid.org/0000-0002-8530-6783)
- Philipp Prucker (ORCID: https://orcid.org/0000-0002-6968-2156)
- Felix J. Dorfner (ORCID: https://orcid.org/0009-0003-5914-3233)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- TUM Klinikum (DE)
- Humboldt-Universität zu Berlin (DE)
- Universitätsklinikum Erlangen (DE)
- Deutsches Herzzentrum München (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Clinical Imaging
- Published
- 2025-07-05
- DOI
- https://doi.org/10.1016/j.clinimag.2025.110557
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Technische Universität München