Comparative analysis of large language models and clinician responses in patient blood management knowledge
LLMs show strong potential for delivering accurate and comprehensive responses to common PBM-related questions. However, it remains essential for clinicians and patients to verify responses, particularly in critical situations.
Authors
- Florian Rumpf (ORCID: https://orcid.org/0000-0002-4698-5808)
- Patrick Meybohm (ORCID: https://orcid.org/0000-0002-2666-8696)
- Kai Zacharowski (ORCID: https://orcid.org/0000-0002-0212-9110)
- Vanessa Neef (ORCID: https://orcid.org/0000-0002-1793-2918)
- Benedikt Schmid (ORCID: https://orcid.org/0000-0003-3413-0690)
- Peter Kranke (ORCID: https://orcid.org/0000-0001-5324-981X)
- Dino Mehic (ORCID: https://orcid.org/0000-0002-3652-1428)
- Suma Choorapoikayil
- Jan Andreas Kloka (ORCID: https://orcid.org/0000-0001-6890-3239)
- Lea Blum
- Philipp Helmer (ORCID: https://orcid.org/0000-0001-7004-7400)
- Tobias E Haas
- Sebastian Hottenrott (ORCID: https://orcid.org/0000-0002-9426-7560)
- Felix Tran
Institutions
- Goethe University Frankfurt (DE)
- Universitätsklinikum Würzburg (DE)
- University Hospital Frankfurt (DE)
Publication Details
- Journal
- Minerva Anestesiologica
- Published
- 2025-08-05
- DOI
- https://doi.org/10.23736/s0375-9393.25.19014-7
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65