评估人工智能驱动的大型语言模型用于正牙美学评分,使用IOTN-AC
1Department of Orthodontics, Faculty of Dentistry, Zonguldak Bulent Ecevit University, Zonguldak 67600, Türkiye.
Diagnostics (Basel, Switzerland)
|December 11, 2025
概括
人工智能 (AI) 大语言模型 (LLM) 在评估正美学方面表现有前途,使用正治疗需求指数 (IOTN-AC). 然而,仅仅是统计准确性就不足以用于临床使用,需要在所有指标上保持一致的高性能.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 牙治疗需求指数 (IOTN-AC) 的美学组成部分对于确定牙治疗的必要性至关重要.
- 在审美评估中评估基于AI的大型语言模型 (LLM) 的准确性对于潜在的临床整合至关重要.
研究的目的:
- 评估AI LLM (ChatGPT-5和ChatGPT-5 Pro) 在评估IOTN-AC.AC.时的准确性.
- 将不同AI模型的性能与经验丰富的临床医生评估进行比较.
主要方法:
- 150张口内照片由两名AI LLMs和两名专家临床医生得分.
- 用IOTN-AC分数和治疗需求分类来评估AI模型的性能.
- 统计分析包括相关性,卡帕,ICC,MAE和布兰德-阿尔特曼分析.
主要成果:
- 两种人工智能模型都显示出与专家评分的显著相关性 (p < 0.001).
- 聊天GPT-5的性能优于聊天GPT-5 Pro,平均绝对误差 (MAE) 较低,分类准确性更高 (66.7%).
- 虽然AI模型通常是公正的,但它们并没有在所有评估参数中始终实现高性能.
结论:
- 人工智能LLM显示出对正牙美学评估的潜力,但需要进一步开发.
- 在安全的临床采用人工智能在正牙科之前,必须在所有指标上保持一致的高性能.
- 仅仅是统计准确性是不够的AI的临床应用在评估正牙治疗需求.
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