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2024年针对感染预防与控制咨询的市售大语言模型准确性的直接比较

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

作者信息

Abosi Oluchi J, Kobayashi Takaaki, Ross Natalie, Trannel Alexandra, Rodriguez Nava Guillermo, Salinas Jorge L, Brust Karen

机构信息

University of Iowa Health Care, Iowa City, IA, USA.

Stanford University, Stanford, CA, USA.

出版信息

Infect Control Hosp Epidemiol. 2024 Dec 12;46(3):1-3. doi: 10.1017/ice.2024.205.

DOI:10.1017/ice.2024.205
PMID:39664019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11883648/
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.

摘要

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。

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JAMA. 2024 Apr 16;331(15):1320-1321. doi: 10.1001/jama.2023.27861.
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Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance.
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Am J Infect Control. 2024 Jun;52(6):625-629. doi: 10.1016/j.ajic.2024.02.007. Epub 2024 Mar 14.
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Accuracy and Reliability of Chatbot Responses to Physician Questions.聊天机器人对医生提问回答的准确性和可靠性。
JAMA Netw Open. 2023 Oct 2;6(10):e2336483. doi: 10.1001/jamanetworkopen.2023.36483.
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An unheard voice: infection prevention professionals reflect on their experiences during the covid-19 pandemic.一个未被听到的声音：感染预防专业人员反思他们在 COVID-19 大流行期间的经历。
Am J Infect Control. 2023 Aug;51(8):890-894. doi: 10.1016/j.ajic.2022.11.021. Epub 2022 Dec 5.
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