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在临床环境中实施人工智能算法:准确性悖论的案例研究.

John A Scaringi1, Ryan A McTaggart1, Matthew D Alvin1

  • 1Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

European radiology
|December 31, 2024
PubMed
概括

一个用于检测大血管阻塞 (LVO) 的人工智能算法被认为是不准确的,原因是错误发现率很高,尽管整体准确性很高. 了解疾病患病率是AI工具在临床实践中采用的关键.

关键词:
人工智能的人工智能是人工智能.诊断错误是一个错误.假阳性反应是一种错误的阳性反应.缺血性中风是因为缺血性中风.积极的预测值是积极的.

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 脑中风的诊断 脑中风的诊断

背景情况:

  • 大血管闭塞 (LVO) 卒中需要快速诊断和治疗.
  • 图像扫描血管造影 (CTA) 对于检测LVO非常重要.
  • 人工智能算法在提高中风检测效率方面表现有前途.

研究的目的:

  • 评估AI算法在紧急情况下检测LVO的性能.
  • 了解为什么该算法受到放射学家的不好评.
  • 确定影响人工智能诊断工具感知精度的因素.

主要方法:

  • 一个单一的三级医院在CTA上部署了LVO检测算法.
  • 一个回顾性分析评估了算法的灵敏度,特异性和预测值.
  • 性能指标与制造商数据和当地疾病流行率进行了比较.

主要成果:

  • 该算法在LVO检测方面实现了100%的灵敏度和92%的特异性.
  • 在当地观察到高错误发现率 (67%) 和低积极预测值 (33%).
  • 这些结果与制造商数据相对比,归因于较低的局部LVO流行率 (4.1%与45.0-62.2%相比).

结论:

  • 人工智能算法被认为是不准确的,原因是高错误发现率,尽管客观准确性.
  • "准确性悖论" (一种基本利率谬误形式) 可能导致了误解.
  • 基于本地疾病患病率的AI性能指标的呈现对于现实的期望和成功的临床整合至关重要.