测试LLM诊断在内牙科:语言变异对未见病例的影响
Itrat Batool1, Nighat Naved1, Fahad Umer1
1Aga Khan University Hospital, Karachi, Pakistan.
International endodontic journal
|February 6, 2026
概括
GPT-5 Plus在牙科病例场景上表现出更高的准确性,但对语言变异敏感. 双子座 2.5 闪光显示在各种变体中表现一致,具有更好的定性推理.
科学领域:
- 人工智能在牙科中的应用
- 临床环境中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于专门领域的诊断支持.
- 对未见的临床数据和语言变异进行LLM绩效评估对于现实应用至关重要.
研究的目的:
- 为了比较GPT-5 Plus和Gemini 2.5闪光器在内牙科和修复牙科病例上的诊断准确度.
- 评估这些模型在临床场景中对语言变异的稳定性.
- 质量评估两个LLMs的推理能力.
主要方法:
- 创建了一个100个牙科MCQ的基准数据集.
- 对数据集进行了语言变化 (转述,扰乱,变换).
- 模型被提示使用案例场景,并计算性能指标 (准确性,F1得分).
- 对推理的定性分析是使用利克尔特尺度进行的.
主要成果:
- 在基准数据集上,GPT-5 Plus实现了80%的准确性,而Gemini 2.5 Flash在66%的准确性.
- 随着语言变异,尤其是扰动,GPT-5 Plus的性能显著下降.
- 双子 2.5 闪存在各种变体中保持了一致的性能.
- 定性分析支持双子座2.5闪光灯的推理质量.
结论:
- GPT-5 Plus在标准数据上表现出色,但易受语言变化的影响.
- 双子座 2.5 闪光显示出对语言变异的更强大的稳定性和优越的定性推理.
- 需要进一步的研究,以了解由于语言现象而导致的LLM退化.
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