评估大型语言模型使用国家内牙科专业考试问题:他们准备好了现实世界牙科吗?
Ezgi Can Çeki̇ç1, Oğuz Tavşan2
1Department of Endodontics, Faculty of Dentistry, Usak University, Usak, 64200, Turkey.
BMC medical education
|October 2, 2025
概括
大型语言模型 (LLM) 显示了内牙教育的潜力,但它们的准确性各不相同. 聊天GPT-4o表现最好,而组合类型的MCQ对所有AI模型来说更具挑战性.
科学领域:
- 人工智能在牙科中的应用
- 医学教育中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 是人工智能系统,越来越多地用于临床支持和教育.
- 在回答各种问题格式,如标准多选题 (MCQs) 和组合类型MCQs (C-MCQs) 时,LLM的可靠性正在受到审查.
- 牙周内教育可以从人工智能工具中受益,但它们的性能需要评估.
研究的目的:
- 评估和比较八个不同的LLM在回答内牙多选题的问题上的表现.
- 在标准MCQ和组合类型MCQ (C-MCQ) 中评估LLM准确性.
主要方法:
- 使用了来自土耳其牙科专业考试 (2012年以来) 的151个内牙科问题.
- 包括ChatGPT-4o,Gemini 1.5 Pro和Copilot在内的8个LLM在土耳其进行了测试.
- 对MCQ和C-MCQ的准确率进行了统计分析.
主要成果:
- 聊天GPT-4o显示了最高的整体准确度 (81.5%),而双子座1.5闪显示了最低的准确度 (57%).
- 在标准MCQ中,ChatGPT-4o显著优于其他模型,但在C-MCQ中没有发现显著差异.
- 与标准MCQ相比,所有LLM在C-MCQ上表现明显差,而Deepseek-V3表现平衡.
结论:
- 在牙科内科,LLM具有潜力作为教育辅助,协助学生和决策.
- 法学士准确性取决于具体的模型和问题类型.
- 作为内牙科的独立资源,LLM尚未可靠.
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