Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy
Overall, LLMs show promise as a tool for patient education in prostate cancer radiotherapy. While improvements are needed in terms of accuracy and readability, positive feedback from clinicians and patients suggests that LLMs can enhance patient understanding and engagement. Further research is essential to fully realize the potential of artificial intelligence in patient education.
Authors
- Sebastian Marschner (ORCID: https://orcid.org/0000-0002-8121-358X)
- Stephan Schönecker (ORCID: https://orcid.org/0000-0003-0359-8960)
- Stefanie Corradini
- Sebastian H. Maier (ORCID: https://orcid.org/0000-0003-0120-8842)
- Claus Belka (ORCID: https://orcid.org/0000-0002-1287-7825)
- Nina Schmidt-Hegemann
- Stefan Brose
- Paul Rogowski (ORCID: https://orcid.org/0000-0003-3351-8896)
- Christian Trapp (ORCID: https://orcid.org/0000-0002-5382-5933)
- Dinah Konnerth
- Maya Rottler
- Michael Keilholz
- Diana-Coralia Dehelean
Institutions
- LMU Klinikum (DE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Strahlentherapie und Onkologie
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1007/s00066-024-02342-3
- Citations
- 12
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 7.85