在 parodontology 知识测试中引领大型语言模型
Ana-Maria Rusa1, Patrick R Schmidlin2, Parthib Sarkar3
1Clinic for Preventive Dentistry, Periodontology, and Cariology, Center of Dental Medicine, University of Zurich, Switzerland. anamaria.rusa@gmail.com.
Swiss dental journal
|January 15, 2026
概括
大型语言模型 (LLM) 显示在牙周病学中适度的知识,但缺乏临床决策的可靠性. 需要进一步的研究来提高它们在牙科专业领域的准确性.
科学领域:
- 人工智能的人工智能
- 牙科研究 牙科研究
- 牙周病学 牙周病学
背景情况:
- 大型语言模型 (LLM) 越来越多地融入临床和教育环境.
- 有限的数据存在于专业牙科领域的LLM表现.
- 评估LLM在牙周病学方面的能力对于了解其潜在应用至关重要.
研究的目的:
- 评估四个大型语言模型 (LLM) 在牙周病学中的表现.
- 在一个验证的问题集上,比较通用和以研究为中心的LLMs.
- 为了确定角色特定原始化对LLM准确性的影响.
主要方法:
- 四个LLM (ChatGPT-4o,DeepSeek-R1,共识,困惑) 在50个多选项牙周科问题上进行了测试.
- 每个LLM都经历了五次独立的试验,在初级化和非初级化条件下.
- 统计分析包括ANOVA和t测试,对难题进行项目级分析.
主要成果:
- 所有LLM的整体准确率为65.0%,模型之间没有显著差异.
- 角色特定的初始化并没有提高LLM的性能.
- 四个问题从未得到正确的答案,其他许多问题显示准确度低,特别是那些需要详细的程序知识或罕见事实的问题.
结论:
- 目前的LLM在牙周病学方面拥有适度的领域知识.
- 在这一领域,LLM还不符合无监督临床决策支持的可靠性标准.
- 需要进一步开发,以提高LLM在专业牙科实践中的准确性和适用性.
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