评估用于感染性内心炎预防的检索增强代大语言模型:临床准确性和效率.
Paak Rewthamrongsris1, Vivat Thongchotchat2, Jirayu Burapacheep3
1Department of Anatomy, Faculty of Dentistry, Center of Artificial Intelligence and Innovation (CAII), Center of Excellence for Dental Stem Cell Biology, Chulalongkorn University, Bangkok, Thailand.
International dental journal
|December 26, 2025
概括
检索增强生成 (RAG) 大语言模型 (LLM) 显示出感染性内心炎 (IE) 预防决策支持的前景. 然而,准确性各不相同,建议在临床使用时谨慎使用.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地使用.
- 检索增强生成 (RAG) 通过在特定的当前数据中对响应进行接地来增强LLM.
- 标准LLM的局限性需要像RAG这样的方法来实现可靠的医疗应用.
研究的目的:
- 为了评估RAG增强的LLMs感染性内心炎 (IE) 预防建议在牙科手术.
- 使用标准化问题集,将RAG-LLM与非RAG LLM的性能进行比较.
- 通过对牙科学生进行试点研究,探索LLMs作为临床决策支持工具的实用性.
主要方法:
- 使用2021年美国心脏协会IE指南测试了十个RAG集成的LLM.
- 从先前的研究中得出的一个一致的IE预防问题被用来进行比较.
- 绩效被评估和没有预提示,和一个试点研究评估了牙科学生的法学士协助.
主要成果:
- 通过预提示,Grok 3β实现了90.0%的准确率;而没有预提示,DeepSeek Reasoner的准确率最高 (83.6%).
- 预提醒通常会提高LLM的准确性,尽管RAG的影响因模型而异.
- 试点研究表明,对准确度的LLM辅助和学生任务时间的显著增加,结果不一.
结论:
- RAG和快速工程可以提高临床决策支持的LLM性能.
- 目前使用RAG的LLM可以快速获取信息,但由于潜在的不准确性,需要进行批判性评估.
- 临床医生和学生在使用这些人工智能工具时必须练习数字素养并保持专业判断力.
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