在牙周炎分期和分级中使用已发表的临床病例对GPT-5的探索性评估
Research square
|February 12, 2026
概括
生成式预训练型变压器5 (GPT-5) 在常见的牙疾病牙周炎的分期和分级中显示出改进的,但有限的准确性. 为了可靠的临床和教育用途,需要进一步开发.
科学领域:
- 人工智能在牙科中的应用
- 自然语言处理应用程序
- 牙周病的诊断 牙周病的诊断
背景情况:
- 牙周炎影响了42%的30岁以上的美国成年人,需要准确的诊断和管理培训.
- 大型语言模型 (LLM) 为牙科教育和临床护理提供了新的机会.
- 晚期LLM,如GPT-5,在牙周炎的诊断性能仍然在很大程度上未被评估.
研究的目的:
- 评估生成预训练型变压器5 (GPT-5) 在分期和分级牙周炎的诊断能力.
- 将GPT-5的性能与牙周炎的确定的诊断标准进行比较.
主要方法:
- 利用了来自谷歌和PubMed的25个牙周炎公共临床病例.
- 采用零射击提示方法,将案例描述输入GPT-5.
- 评估了GPT-5的预测与使用准确度和科恩卡帕的参考诊断.
主要成果:
- GPT-5表现出取决于类别的表现,倾向于高估疾病严重程度.
- 在分阶段 (kappa=0.432) 和分级 (kappa=0.179) 方面获得了68.0%的精度和77.3%的精度.
- 分期显示了公平的协议,而分级表明可靠性差和临床实用性有限.
结论:
- 与之前的模型相比,GPT-5提供了更好的性能,但存在局限性,特别是在分级牙周炎的严重程度方面.
- 目前的诊断准确性限制了GPT-5在临床评估和牙科教育中的直接应用.
- 在可靠性和验证方面取得重大进展对于将LLMs纳入牙周诊断和培训至关重要.
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