Evaluating interactions of patients with large language models for medical information
This study highlights the potential of LLMs to enhance patient education and communication in clinical settings, with patients valuing their user-friendliness and comprehensiveness for medical information. By addressing preliminary questions, LLMs could potentially relieve time constraints on healthcare providers, enabling medical personnel to focus on complex inquiries and patient care.
Authors
- Lisa Nguyen (ORCID: https://orcid.org/0000-0002-9272-3980)
- Maurice Stephan Michel (ORCID: https://orcid.org/0000-0001-7424-9085)
- Martin J. Hetz
- Friedrich Otto Hartung (ORCID: https://orcid.org/0000-0003-1112-0705)
- Tim Holland‐Letz (ORCID: https://orcid.org/0000-0003-0348-8163)
- Frederik Wessels (ORCID: https://orcid.org/0000-0003-4213-8692)
- Christoph Wies (ORCID: https://orcid.org/0000-0001-7136-298X)
- Britta Grüne (ORCID: https://orcid.org/0000-0003-1593-217X)
- Sarah Haggenmüller (ORCID: https://orcid.org/0000-0002-7961-1781)
- Titus Josef Brinker (ORCID: https://orcid.org/0000-0002-3620-5919)
- Nicolas Carl (ORCID: https://orcid.org/0009-0002-0080-3455)
- Jonathan Winterstein
- Maurin Helen Mangold
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- University Medical Centre Mannheim (DE)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1111/bju.16676
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 5.23