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Assessment of the Mouth01:26

Assessment of the Mouth

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A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
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增强的大型语言模型在假牙多选题上的表现

Shenghan Gao1, Zi-Ang Wang2, Zihan Gao1

  • 1Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, PR China.

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PubMed
概括

使用检索增强生成 (RAG),上下文学习 (ICL) 和多数投票的增强大型语言模型 (LLM) 显示,在中文假牙问题上的准确性有所提高,但在英语问题上没有. 这些增强的LLM显示出牙科教育任务的潜力.

关键词:
人工智能的人工智能是人工智能.教育教育教育教育教育教育.大型语言模型.牙修复 牙修复 牙修复 牙修复 牙修复检索增强生成 - 搜索增强生成

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科学领域:

  • 人工智能在牙科中的应用
  • 用于牙科教育的自然语言处理.
  • 大型语言模型的性能评估.

背景情况:

  • 大型语言模型 (LLM) 越来越多地被用于牙科等专业领域.
  • 提升策略,如检索增强生成 (RAG),上下文学习 (ICL) 和多数投票,旨在提高LLM的准确性和可靠性.
  • 评估LLM在特定领域问题上的表现对于其实际应用至关重要.

研究的目的:

  • 评估检索增强生成 (RAG),上下文学习 (ICL) 和多数投票在增强大语言模型 (LLMs) 对于假牙问题的有效性.
  • 为了比较增强的LLMs与它们的基础版本的性能,使用中文和英语的假牙问题集.
  • 分析错误类型并确定LLM增强带来显著改进的领域.

主要方法:

  • 使用RAG,ICL和多数投票技术增强了两个基本的大型语言模型 (OpenAI o1,DeepSeek-R1).
  • 标准化中文多选题 (C-MCQs) 和英语多选题 (E-MCQs) 在假牙术中被用于评估.
  • 通过对基础模型和增强模型的答案正确性,错误分析和统计比较 (χ2测试) 来衡量性能.

主要成果:

  • 增强的LLM在C-MCQ上显示了统计学上显著的准确性改善 (P < .001) 和基于知识的错误的减少 (P < .008).
  • 虽然增强的LLM在E-MCQ中显示出更高的准确性,但这种差异在统计学上并不显著 (P = .145).
  • 提高LLM的好处,特别是在准确性和减少错误方面,主要是在中文问题集中观察到的.

结论:

  • 推断时间增强策略 (RAG,ICL,多数投票) 显著提高了对中国假牙问题的LLM准确性.
  • 这些改进有效地减轻了某些错误,但并不总是为英语问题带来统计学上显著的改进.
  • 增强的LLM显示出处理牙科相关任务的前景,并有可能在牙科教育中应用.