Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models
Question Can locally deployed open-weight large language models (LLMs) improve the readability and understandability of radiology reports for medical laypersons at a level comparable to closed-weight models? Findings LLMs significantly improved quantitative readability scores and qualitative ratings of layperson understandability; Llama-3-70B and GPT-4o showed comparable performance, and although the open-source models exhibited a higher error rate, they still performed well overall. Clinical relevance Open-weight LLMs provide a privacy-compliant way to generate a template for patient-friendly radiology reports suitable for real-world clinical use.
Authors
- Maike Theis (ORCID: https://orcid.org/0000-0003-2666-9928)
- Julian Alexander Luetkens (ORCID: https://orcid.org/0000-0002-7715-4636)
- Dmitrij Kravchenko (ORCID: https://orcid.org/0000-0003-0331-2045)
- Tatjana Dell (ORCID: https://orcid.org/0000-0002-2835-8560)
- Taraneh Aziz-Safaie
- Claus Christian Pieper (ORCID: https://orcid.org/0000-0002-0893-1200)
- Sebastian Nowak (ORCID: https://orcid.org/0000-0001-5175-5559)
- Narine Mesropyan (ORCID: https://orcid.org/0000-0002-5871-3965)
- D Kütting
- Alois M. Sprinkart (ORCID: https://orcid.org/0000-0002-1435-9562)
- Babak Salam
- Mohammed Hayawi
- Annemarie Katharina Proff
Institutions
- University Hospital Bonn (DE)
- Universitätsklinikum Aachen (DE)
Publication Details
- Journal
- European Radiology
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1007/s00330-026-12329-6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00