Harnessing advanced large language models in otolaryngology board examinations: an investigation using python and application programming interfaces
While newer LLMs show strong potential in addressing specialized medical content, the observed decline in GPT-3.5 Turbo's performance over time underscores the necessity for continuous evaluation. This study highlights the critical need for ongoing optimization and efficient API usage to improve LLMs potential for applications in medical education and certification.
Authors
- Markus Wirth
- Barbara Wollenberg (ORCID: https://orcid.org/0000-0002-3062-459X)
- Jan-Christoffer Lüers (ORCID: https://orcid.org/0000-0002-8273-2234)
- Gerd Fabian Volk (ORCID: https://orcid.org/0000-0003-1245-6331)
- Benedikt Schmidl (ORCID: https://orcid.org/0000-0003-2201-4246)
- Michael G. Alfertshofer (ORCID: https://orcid.org/0000-0002-4892-2376)
- Timon Hussain (ORCID: https://orcid.org/0000-0003-4256-232X)
- Orlando Guntinas‐Lichius (ORCID: https://orcid.org/0000-0001-9671-0784)
- Cosima C. Hoch (ORCID: https://orcid.org/0000-0002-3875-7389)
- Paul F. Funk (ORCID: https://orcid.org/0009-0000-4316-4249)
Institutions
- University of Cologne (DE)
- Humboldt-Universität zu Berlin (DE)
- Jena University Hospital (DE)
- Technical University of Munich (DE)
- Friedrich Schiller University Jena (DE)
Publication Details
- Journal
- European Archives of Oto-Rhino-Laryngology
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1007/s00405-025-09404-x
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Technische Universität München