Abosi Oluchi J, Kobayashi Takaaki, Ross Natalie, Trannel Alexandra, Rodriguez Nava Guillermo, Salinas Jorge L, Brust Karen
University of Iowa Health Care, Iowa City, IA, USA.
Stanford University, Stanford, CA, USA.
Infect Control Hosp Epidemiol. 2024 Dec 12;46(3):1-3. doi: 10.1017/ice.2024.205.
We investigated the accuracy and completeness of four large language model (LLM) artificial intelligence tools. Most LLMs provided acceptable answers to commonly asked infection prevention questions (accuracy 98.9%, completeness 94.6%). The use of LLMs to supplement infection prevention consults should be further explored.
我们调查了四种大语言模型(LLM)人工智能工具的准确性和完整性。大多数大语言模型对常见的感染预防问题给出了可接受的答案(准确率98.9%,完整性94.6%)。应进一步探索使用大语言模型来补充感染预防咨询。