大型语言模型在儿科牙科的诊断能力
Tatsuya Akitomo1, Ami Kaneki1, Taku Nishimura1
1Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
International dental journal
|February 21, 2026
概括
大型语言模型 (LLM) 对儿科牙科诊断有前途,在识别问题方面达到70%以上的准确性. 然而,在没有主要投诉的情况下,准确性会大幅下降,特别是在复杂的儿科病例中.
科学领域:
- 人工智能在牙科中的应用
- 儿科牙科应用 儿科牙科应用
- 医疗诊断工具 医疗诊断工具
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于牙科诊断.
- 现有的研究往往集中在单一的主题上,限制了对更广泛临床实用性的理解.
- 在不同儿科牙科患者场景中LLM的应用仍然未得到充分研究.
研究的目的:
- 评价 pediatric dentistry 中两个LLM (Copilot 和 Gemini) 的诊断准确性.
- 评估包括或省略患者首席投诉对LLM诊断绩效的影响.
- 确定LLM在儿科牙科诊断方面擅长或扎的特定领域.
主要方法:
- 从国家检查中选择了25个儿科牙科病例.
- 患者数据 (年龄,性别,主要投诉) 与口内照片和X射线图一起提交给LLM.
- 在不同信息条件下,LLM的任务是识别异常区域并提供诊断.
主要成果:
- 两种LLM都在识别病理位置时表现出>70%的准确性,在诊断时表现出~60%的准确性.
- 当首要的投诉被遗漏时,诊断准确性显著下降.
- 在儿科特异性问题上,如牙数异常和喷发障碍等问题上,LLM的表现不稳定,没有首席投诉.
结论:
- 在儿童牙科中,LLM具有潜力,但准确的首席投诉对于最佳表现至关重要.
- 目前,LLM在诊断复杂的儿科专用牙科疾病方面存在局限性.
- 需要进一步发展,以提高专业儿科牙科的LLM能力.
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