使用多模式语言模型的正统牙生物机械推理:性能和临床实用性
Arda Arısan1, Celal Genç2, Gökhan Serhat Duran2
1Independent Researcher, Ankara 06510, Turkey.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
多模式大语言模型 (LLM) 显示了正统牙科生物机械分析的潜力. GPT-o3在评估正牙力系统方面表现最高,这表明未来的临床决策支持能力.
科学领域:
- 人工智能在牙科中的应用
- ортодонтическая 生物力学 ортодонтическая 生物力学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 多模式大语言模型 (LLM) 正在成为临床支持工具.
- 它们对正义牙生物机械推理的能力仍然未被评估.
- 这项研究评估了LLMs分析正治疗机制的能力.
研究的目的:
- 系统地评估多式大型语言模型 (LLM) 对于正统牙科生物机械推理的能力.
- 探索LLMs在支持正义牙科决策方面的潜在作用.
- 为了比较不同LLM在分析正力系统中的性能.
主要方法:
- 五个多式大型语言模型 (LLM) 分析了56张标准化口腔内照片的正牙力系统.
- 三位经验丰富的正牙医使用5分尺度评估了观察,解释,生物力学和信心的模型输出.
- 进行了评估者之间的可靠性和模型之间的统计比较.
主要成果:
- GPT-o3获得了最高的综合分数 (66.8%),明显超过了克劳德 (57.8%),双子座 (52.6%),GPT-4.0 (48.8%) 和格洛克 (38.8%).
- 在专家评价者中观察到卓越的评价者之间的可靠性 (ICC:0.786-0.802).
- 模型自我报告的信心与专家评级的输出质量相关性很差.
结论:
- 多模式大语言模型 (LLM) 显示了协助正统牙科生物机械评估的新兴潜力.
- 专家指导的验证可以增强LLM对牙矯正临床决策支持的贡献.
- 在日常的正牙护理中,LLM可以在各种生物机械场景中提供有价值的支持.
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