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传染性内心炎大型语言模型的准确性牙科手术中的预防性内心炎

Paak Rewthamrongsris¹, Jirayu Burapacheep², Vorapat Trachoo³

¹Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.

International dental journal

|October 12, 2024

概括

大型语言模型 (LLM) 显示了为预防传染性内心炎 (IE) 提供抗生素预防信息的潜力. 虽然GPT-4o实现了80%的准确性,但预提示和特定领域的培训对于提高医疗保健LLM可靠性至关重要.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
牙科公共卫生牙科公共卫生

背景情况:

传染性内心炎 (IE) 需要对接受牙科手术的高风险患者进行抗生素预防.
牙科专业人员越来越多地使用大型语言模型 (LLM).
对IE预防的准确信息检索对于患者安全至关重要.

研究的目的:

根据2021年AHA指南,评估受欢迎的LLM在回答有关抗生素预防IE预防问题的准确性.
评估预促策略对LLM绩效的影响.

主要方法:

使用了来自2021年AHA IE指南的28个真假问题.
七个LLM使用两个提示策略进行了测试:带有和没有预提示模拟经验丰富的牙医.
每个问题独立地运行了五次,统计分析比较模型准确性.

主要成果:

在LLM中发现了显著的准确性差异;GPT-4o (80%) 和Gemini 1.5 Pro (78.57%) 在预提示中表现最好.
预提示通常缩小了大多数模型的置信区间,并改善了大多数模型的性能.
没有LLM始终达到基准分数,这表明准确度的变化和需要进一步优化.

结论:

像GPT-4o这样的LLM在访问IE预防指南方面表现有前途,达到高达80%的准确性.
提前提示策略可以提高LLM的性能,但复杂的医疗查询仍然具有挑战性.
针对特定领域的培训对于优化临床决策支持中的LLM准确性和可靠性至关重要.

关键词:

根据AHA的指导方针.人工智能的人工智能是人工智能.聊天GPT 聊天在GPT 聊天克劳德·克劳德·克劳德 (Claude Claude) 是一个双子座是一个双子座.

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