放射学解释的新前沿:评估大型语言模型在肺胸部诊断中的有效性
Bensu Bulut1, Medine Akkan Öz1, Murat Genç2
1Department of Emergency Medicine, Ankara Gulhane Training and Research Hospital, Health Science University, Ankara, Turkey.
PloS one
|September 12, 2025
概括
三个大型语言模型 (LLM) 显示了在成年人胸部放射图中识别肺胸部的潜力,ChatGPT-4o表现最好. 然而,诊断准确性在儿科患者和小肺胸部患者显著下降,需要进一步验证.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 多模式大语言模型 (LLM) 是新兴的工具,在医学诊断中具有潜在的应用.
- 评估高级LLM的诊断性能,如ChatGPT-4o,Gemini 2.0和Claude 3.5,在确定特定的医疗条件方面至关重要.
研究的目的:
- 评估三个领先的多式联络LLM的诊断准确性,用于从胸部X射线图检测肺胸部.
- 为了比较ChatGPT-4o,Gemini 2.0和Claude 3.5在不同患者年龄组和肺胸大小中的性能.
主要方法:
- 追溯分析包括172例肺胸病例 (成人和儿科患者),胸部放射和CT扫描.
- 通过ChatGPT-4o,Gemini 2.0和Claude 3.5分析了胸部放射图,并附带了基本的临床症状.
- 使用整体,严格和理想准确度指标测量诊断准确度,通过Fleiss' Kappa评估响应的一致性.
主要成果:
- 在成年患者中,ChatGPT-4o获得了最高的整体精度 (69.6%),超过了Gemini 2.0 (57.4%) 和Claude 3.5 (64.9%).
- 所有模型在儿科患者 (20.8%或更低) 中表现明显较低.
- 与小肺胸 (42.2%) 相比,ChatGPT-4o对成年人 (81.6%) 的大肺胸部显示出更高的准确性. 双子座2.0显示出大型肺胸部 (卡帕=1.00) 的优异一致性.
结论:
- 目前的多式联络式LLM显示出临床应用的希望,用于识别肺胸部,特别是在患有大型肺胸部的成年患者中.
- 儿科病例和小肺胸部的性能限制需要进一步的研究和验证.
- 该研究强调,需要谨慎地实施放射学LLM,强调年龄和特定条件的绩效评估.
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