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。

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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引用次数: 0

摘要

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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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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来源期刊

Infection Control and Hospital Epidemiology 医学-传染病学

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.