导航在放射学人工智能实施的前沿 - 第一部分:绩效评估
Maguy Farhat1,2, Samir A Dagher1, Burak Berksu Ozkara1,3
1Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, USA.
The neuroradiology journal
|December 18, 2025
概括
医学成像中的人工智能 (AI) 由于性能评估问题,在临床使用中面临挑战. 这项研究涉及数据异质性,指标和访问,以改善放射学中的AI部署.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 尽管人工智能研究和医疗成像投资显著增长,但临床翻译仍然有限.
- 由于缺乏标准化指导方针来评估AI模型性能和道德考虑,存在一个关键的差距.
研究的目的:
- 为阻碍在放射学中实施人工智能的性能挑战提供一个实际的视角.
- 探索应对这些挑战的缓解策略,重点关注数据异质性,绩效指标和数据访问.
主要方法:
- 在放射学中探索AI性能评估中的挑战.
- 专注于数据异质性,性能指标的选择和可解释性以及数据访问问题.
- 讨论克服这些障碍的潜在机会.
主要成果:
- 确定了人工智能在放射学中的关键绩效评估挑战,包括数据可变性和指标解释.
- 强调需要改进数据访问和标准化绩效指标.
- 强调了人工智能的理论潜力与其实际临床应用之间的差距.
结论:
- 解决绩效评估的挑战对于AI在临床放射学中的实际整合至关重要.
- 确定的准则对于AI在医学成像中的安全和高效部署至关重要.
- 缩小人工智能潜力和临床实施之间的差距,需要集中精力在性能和数据标准化上.
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