Evaluating the role of large language models in supporting patient education during the informed consent process for routine radiology procedures
This study evaluates the potential of LLMs to enhance informed consent in radiology, highlighting strengths in general and technical questions while noting limitations with complex clinical inquiries, with performance varying significantly by model type and imaging modality.
Authors
- Eric Einspänner (ORCID: https://orcid.org/0000-0003-2363-5132)
- Sebastian Hupfeld (ORCID: https://orcid.org/0000-0002-6034-5161)
- Jan Borggrefe (ORCID: https://orcid.org/0000-0003-2908-7560)
- Maximilian Thormann (ORCID: https://orcid.org/0000-0003-3822-8871)
- Daniel Behme (ORCID: https://orcid.org/0000-0002-5353-9515)
- Matthias Gawlitza (ORCID: https://orcid.org/0000-0002-1229-3352)
- Roland Schwab (ORCID: https://orcid.org/0009-0008-3799-6194)
- Erelle Fuchs
Institutions
- University Hospitals of the Ruhr-University of Bochum (DE)
- Jena University Hospital (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1093/bjr/tqaf225
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65