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多模态大语言模型用于囊膜图像解释和膀损伤分类:比较研究

Yung-Chi Shih1, Cheng-Yang Wu2, Shi-Wei Huang2,3

  • 1Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan.

Journal of medical Internet research
|January 28, 2026
PubMed
概括

多模式大语言模型 (MM-LLMs) 在解释囊镜图像方面表现有前途,有助于病变检测和活检决策. 然而,它们对复杂病例的诊断推理在临床使用中需要进一步改进.

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在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.活检是指标中的一个.膀瘤是什么意思 膀瘤是什么意思囊镜检查 囊镜检查 囊镜检查诊断推理 诊断推理 诊断推理发现描述 发现描述大型语言模型这是一个多式联络模式.

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科学领域:

  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析
  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科

背景情况:

  • 囊镜是诊断膀病变的标准,但取决于操作者,可以忽略微妙的异常.
  • 当前的人工智能系统缺乏解释能力和对囊镜检测结果的解释性报告.
  • 多模式大型语言模型 (MM-LLM) 提供集成的视觉识别,推理和语言生成,以增强解释.

研究的目的:

  • 评估最新的MM-LLM用于囊膜图像解释和病变分类.
  • 用具有挑战性的,临床医生定义的数据集来评估诊断准确性,推理质量和临床相关性.
  • 为了比较不同MM-LLM在特定诊断任务上的性能.

主要方法:

  • 四个MM-LLM (OpenAI-o3,ChatGPT-4o,Gemini 2.5 Pro,MedGemma-27B) 在自由文本解释 (n=401) 和7类损伤分类 (n=113) 上进行了测试.
  • 输出被盲目的评分员使用5分利克特级别来评分.
  • 包括准确性,敏感性,特异性,Youden J和MCC在内的分类指标被计算为病变检测,活检指示和恶性病变.

主要成果:

  • 在解剖学和发现解释方面,OpenAI-o3表现出强的表现,但在更高层次的推理和诊断方面遇到了困难.
  • 对于临床相关的终点,OpenAI-o3获得了最平衡的性能,病变检测准确率为88.3%.
  • 在7类分类中,OpenAI-o3的表现优于其他模型,特别是在恶性病变方面,尽管恶性病变的整体准确率为62.8%.

结论:

  • MM-LLM显示出作为囊镜解释,活检分类和培训的辅助工具的巨大潜力.
  • 目前MM-LLM在复杂差异诊断中的性能不佳,需要进一步优化.
  • 临床整合需要进一步发展,以确保在诊断膀病变时的安全和可靠应用.