大型语言模型的诊断性能在放射性病变的多式分析中
1Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Republic of Korea.
International dental journal
|September 17, 2025
概括
在使用多模式成像来诊断放射性下病变方面,ChatGPT的表现优于Gemini. 增加的成像数据和多选题格式显著提高了大型语言模型 (LLM) 的诊断准确性.
科学领域:
- 人工智能在医学中的应用
- 口腔和牙面部外科手术
- 医学诊断 医学诊断 医学诊断
背景情况:
- 大型语言模型 (LLM) 在医学诊断方面表现有前途,但它们在口腔和面外科手术 (OMFS) 中的多模式成像效应尚未得到充分探索.
- 放射性下病变存在诊断上的挑战,因为有重叠的放射性特征.
研究的目的:
- 评估ChatGPT 4o和Gemini 2.5 Pro在OMFS放射性下病变病例中的诊断性能.
- 为了比较不同成像条件 (全景放射,CT,病理学) 和问题格式 (MCQ,SAQ) 的模型准确性.
主要方法:
- 分析了100个匿名的OMFS病例,其中有放射性病变.
- 在MQQ和SAQ格式中,ChatGPT和Gemini使用全景放射 (P),P+CT (P+C) 和P+CT+病理 (P+C+B) 处理了病例.
- 模型的准确性与专家诊断相比得分;使用麦克纳马尔和科克兰的Q测试来评估统计差异.
主要成果:
- 在MCQ (82%对63%最大) 和SAQ (48%对28%最大) 格式中,ChatGPT的准确度高于Gemini.
- 通过额外的成像数据,ChatGPT的诊断准确性显著提高 (P <.001对于MCQ).
- 与SAQ相比,MCQ格式表现出更高的性能,突出显示了人类在循环中集成的好处.
结论:
- 对于OMFS放射性部病变,ChatGPT对双子星表现出优越的诊断性能,特别是在多式成像输入的情况下.
- 在OMFS中,LLM显示出作为诊断辅助工具的潜力,通过结构化数据和人循环格式来提高准确性.
- 对于临床采用,需要进一步验证更大的数据集和混合人工智能系统.
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