Performance of Publicly Available Large Language Models on Internal Medicine Board-style Questions

Ongoing research attempts to benchmark large language models (LLM) against physicians’ fund of knowledge by assessing LLM performance on medical examinations. No prior study has assessed LLM performance on internal medicine (IM) board examination questions. Limited data exists on how knowledge suppl...

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Veröffentlicht in:	PLOS digital health Jg. 3; H. 9; S. e0000604
Hauptverfasser:	Tarabanis, Constantine, Zahid, Sohail, Mamalis, Marios, Zhang, Kevin, Kalampokis, Evangelos, Jankelson, Lior
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.09.2024
Schlagworte:	Accuracy Application programming interface Artificial intelligence Benchmarks Biology and Life Sciences Chatbots Infectious diseases Internal medicine Large language models Medicine Medicine and Health Sciences Multiple choice People and Places Social Sciences Standardized tests
ISSN:	2767-3170, 2767-3170
Online-Zugang:	Volltext
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