Harnessing advanced large language models in otolaryngology board examinations: an investigation using python and application programming interfaces

Purpose This study aimed to explore the capabilities of advanced large language models (LLMs), including OpenAI’s GPT-4 variants, Google’s Gemini series, and Anthropic’s Claude series, in addressing highly specialized otolaryngology board examination questions. Additionally, the study included a lon...

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Veröffentlicht in:	European archives of oto-rhino-laryngology Jg. 282; H. 6; S. 3317 - 3328
Hauptverfasser:	Hoch, Cosima C., Funk, Paul F., Guntinas-Lichius, Orlando, Volk, Gerd Fabian, Lüers, Jan-Christoffer, Hussain, Timon, Wirth, Markus, Schmidl, Benedikt, Wollenberg, Barbara, Alfertshofer, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Schlagworte:	Certification Educational Measurement - methods Germany Head and Neck Surgery Humans Large Language Models Medicine Medicine & Public Health Miscellaneous Neurosurgery Otolaryngology - education Otorhinolaryngology Programming Languages Specialty Boards Germany Board examinations Python programming Medical AI integration Large language models Otolaryngology education APIs
ISSN:	0937-4477, 1434-4726, 1434-4726
Online-Zugang:	Volltext
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