通过可视化大型语言模型的胸部X射线解释的局限性,双子座1.0,双子座1.5 Pro,GPT-4 Turbo和GPT-4o
Chih-Hsiung Chen1, Chang-Wei Chen2, Kuang-Yu Hsieh1
1Department of Critical Care Medicine, Mennonite Christian Hospital, Hualien 970472, Taiwan.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
大型语言模型 (LLM) 在解释胸部X射线 (CXR) 中表现有前途,但在小病变和特定细节方面扎. 双子1.5 Pro在性能方面领先,尽管需要进一步的AI开发才能完成CXR分析.
科学领域:
- 医疗成像中的人工智能
- 计算机视觉用于诊断放射学.
背景情况:
- 准确的胸部X射线 (CXR) 解释对于诊断至关重要,需要识别病变,诊断,位置,大小和数量.
- 在没有临床背景的情况下,仅用于图像CXR解释的可视化大语言模型 (LLM) 的有效性尚未得到充分证实.
研究的目的:
- 评估领先的LLM在仅基于图像数据的CXR解释方面的表现.
- 评估LLM在初级诊断和识别各种肺部疾病中的关键成像特征中的准确性.
主要方法:
- 通过Gemini 1.0,Gemini 1.5 Pro,GPT-4 Turbo和GPT-4o对13个诊断类别的247个CXR进行了分析.
- 评估了LLM输出的主要诊断准确性 (完全正确,部分正确,不正确) 和关键特征识别.
- 像成像视图和设备这样的非诊断特征被单独分析.
主要成果:
- 在检测大型,双边和多重病变以及突出的设备 (例如肺,起器) 方面,LLM表现出更高的灵敏度.
- 双子 1.5 Pro实现了最高的总体检测率,其次是双子 1.0,GPT-4o和GPT-4 Turbo.
- 模型的性能在描述视图和标记等成像特征方面有所不同,在识别特定的医学术语方面有一些局限性.
结论:
- LLM可以识别某些CXR诊断和特征,特别是在显著异常的情况下.
- 在检测小病变,确定横向性和复杂的诊断推理方面仍然存在局限性.
- 对于LLM来说,在没有附带临床信息的情况下可靠地解释CXR,需要进一步的进展.
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