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相关概念视频

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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基于大型语言模型的不确定性调整标签提取用于人工智能模型开发在上肢放射学上.

Hanna Kreutzer1,2, Anne-Sophie Caselitz3,4, Thomas Dratsch5

  • 1Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany. hannkreutzer@ukaachen.de.

European radiology
|November 14, 2025
PubMed
概括

GPT-4o可以准确地从放射学报告中提取诊断标签,从而实现具有竞争力的多标签图像分类模型. 报告中的不确定性没有影响模型性能,展示了AI.

关键词:
人工智能的人工智能是人工智能.大型语言模型.放射学 放射学 放射学 放射学上肢的上端部分.

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科学领域:

  • 放射学中的人工智能
  • 机器学习用于医学成像
  • 在医疗保健中的自然语言处理.

背景情况:

  • 放射学报告包含有价值的诊断信息.
  • 从自由文本报告中提取结构化数据是一项挑战.
  • 人工智能模型需要大型,准确标记的数据集进行训练.

研究的目的:

  • 评估GPT-4o在从放射学报告中提取结构化诊断标签时的零射击能力.
  • 为了评估这些提取的标签的影响,包括不确定性,对多标签图像分类性能肌肉骨放射.

主要方法:

  • 关节骨,肘部和指的放射图的回顾性分析.
  • GPT-4o从匿名报告中提取了标签 (存在,缺席,不确定).
  • 不确定性标签被包容性和专为模型培训而处理.
  • 使用ResNet50架构进行多标签分类.
  • 在使用AUC和其他指标的内部和外部测试集上验证了性能.

主要成果:

  • 在测试组中,GPT-4o在自动标签提取中实现了>98%的准确性.
  • 基于标签的模型显示出具有竞争力的性能 (例如,肘部AUC=0.80),无论不确定性处理如何.
  • 模型很好地对外部数据集进行了概括,具有一致的性能.
  • 在标签策略或数据集之间没有观察到显著的性能差异 (p≥0.15).

结论:

  • GPT-4o有效地从放射学报告中提取高精度的诊断标签.
  • 这些标签有助于培养具有竞争力的多标签图像分类模型.
  • 提取标签中的不确定性处理没有显著影响模型性能.