评估人工智能驱动的放射学诊断工具的性能和临床实用性
Sarah F Mercaldo1,2,3, James M Hillis1,3,4, Jeffrey D Blume5
1Digital CRO, Mass General Brigham AI, 399 Revolution Dr, Somerville, MA 02145.
Radiology
|November 25, 2025
概括
在放射学中评估人工智能 (AI) 工具对于安全的临床整合至关重要. 本指南提供了评估AI模型性能和临床影响的实用策略,确保可靠的诊断进步.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 工具越来越多地集成到放射学工作流程中.
- 人工智能在准确性,效率和患者护理方面提供了潜在的好处.
- 在临床采用AI工具之前,严格的评估是必不可少的.
研究的目的:
- 为在放射学中评估人工智能工具提供实用指导.
- 概述人工智能模型在其整个生命周期的评估的结构化方法.
- 在AI评估中支持放射科医生,研究人员和审稿人.
主要方法:
- 审查AI评估原则,包括内部和外部测试.
- 讨论特定于AI输出的性能指标 (分类,检测,细分).
- 描述评估临床影响的方法,例如多读者多病例研究.
主要成果:
- 概述了人工智能评估的关键原则.
- 为AI量身定制的性能指标被突出显示.
- 评估临床效用和影响的方法详细说明.
结论:
- 结构化的评估对于人工智能在放射学中安全有效地整合至关重要.
- 本书将统计学概念转化为放射学特定的AI评估指南.
- 高质量的AI评估研究可以通过明确的方法和报告标准来实现.
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