A head-to-head comparison of the accuracy of commercially available large language models for infection prevention and control inquiries, 2024.

IF 3 4区 医学 Q2 INFECTIOUS DISEASES Infection Control and Hospital Epidemiology Pub Date : 2024-12-12 DOI:10.1017/ice.2024.205
Oluchi J Abosi, Takaaki Kobayashi, Natalie Ross, Alexandra Trannel, Guillermo Rodriguez Nava, Jorge L Salinas, Karen Brust
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Abstract

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.

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用于感染预防和控制查询的商用大型语言模型的准确性的正面比较,2024。
我们研究了四种大型语言模型(LLM)人工智能工具的准确性和完整性。大多数法学硕士对常见感染预防问题提供了可接受的答案(准确率98.9%,完整性94.6%)。应进一步探索利用法学硕士补充感染预防会诊。
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来源期刊
CiteScore
6.40
自引率
6.70%
发文量
289
审稿时长
3-8 weeks
期刊介绍: Infection Control and Hospital Epidemiology provides original, peer-reviewed scientific articles for anyone involved with an infection control or epidemiology program in a hospital or healthcare facility. Written by infection control practitioners and epidemiologists and guided by an editorial board composed of the nation''s leaders in the field, ICHE provides a critical forum for this vital information.
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