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标准和协议:评估在感知EULAR 2019年狼分类中的生成人工智能的有效性

Gerald H Lushington1, Sandeep Nair1, Eldon R Jupe1

  • 1Progentec Diagnostics Inc., Oklahoma City, OK 73104, USA.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
概括

生成性AI (genAI) 对于识别系统性红斑狼 (SLE) 的消极性具有很高的灵敏度,但与积极性扎. 人工智能能自信地识别出SLE阳性病例,但可能错误地将一些人归类为不分化结缔组织疾病 (UCTD).

关键词:
美国风湿病学学院 (ACR)欧洲风湿病学协会联盟 (EULAR)抗核抗体 (ANA) 是一种抗核抗体.疾病分类疾病分类.生成型的人工智能 (genAI)自然语言处理 (NLP)系统性红斑狼 (SLE) 是一种不分化连接组织疾病 (UCTD)

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的人工智能
  • 类风湿病学 类风湿病学

背景情况:

  • 电子健康记录 (EHR) 和传统医疗记录 (MR) 在高效的数据处理方面存在挑战.
  • 过度文档的信息过载阻碍了临床工作流程.
  • 生成性AI (genAI) 提供了自动化MR总结和表征的潜力.

研究的目的:

  • 评估两通的genAI策略对系统性红斑狼 (SLE) 的分类的有效性.
  • 通过EULAR 2019标准评估genAI在区分SLE,不分化的结缔组织疾病 (UCTD) 和非SLE病例方面的表现.
  • 在评估特定的SLE诊断标准时分析genAI的性能指标.

主要方法:

  • 78个个人的医疗记录 (MR) 的数字化.
  • 实施使用Claude 3.5 LLM的双通生成AI (genAI) 评估.
  • 应用22个标准的EULAR 2019模型来对SLE进行分类.

主要成果:

  • 抗核抗体 (ANA) 标准显示SLE负性的高灵敏度 (0.78) 和PPV (0.85),但边际特异性 (0.60).
  • 通过21个标准的平均值,genAI对SLE阳性呈现出高特异性 (0.54),但倾向于将阳性结果错误分类为UCTD.
  • GenAI表现出自信的SLE负面评估 (高灵敏度),但积极性评估较弱.

结论:

  • 基因AI为SLE消极性提供了自信的评估,但对积极性不那么可靠.
  • 人工智能的特异性支持自信的SLE阳性断言,但可能导致错误地将临床阳性归类为UCTD.
  • 为了准确的SLE分类,需要进一步完善genAI模型,特别是区分SLE和UCTD.