机器幻觉在放射学中的分类
Frank J Brooks1, Mark A Anastasio1
1Department of Bioengineering, Grainger College of Engineering, University of Illinois Urbana-Champaign, 1406 W. Green St, 1102 Everitt Lab MC 278, Urbana, IL 61801.
Radiology. Artificial intelligence
|March 11, 2026
概括
在生成AI中定义机器幻觉对于放射学至关重要. 这项研究澄清了不同的解释,以提高AI可信度和临床使用的沟通.
科学领域:
- 医疗成像中的人工智能
- 放射学 人工智能 评估
- 生成式人工智能性能指标
背景情况:
- 在放射学中评估生成性AI需要对机器幻觉有明确的定义.
- 目前幻觉定义的模糊性阻碍了可靠的AI评估和临床采用.
- 对幻觉的不同解释导致了相互矛盾的AI系统特征.
研究的目的:
- 在AI中提供不同机器幻觉概念的非技术解释.
- 为放射科医生提出与机器幻觉相关的分类学.
- 在放射学中评估生成AI性能时减少模两可.
主要方法:
- 解释机器幻觉的两个概念之间的根本差异.
- 使用放射学家熟悉的术语开发分类学.
- 对部署的人工智能系统的输出应急情况的划分.
主要成果:
- 澄清机器幻觉定义中的核心差异.
- 一个拟议的分类学来根据解释对AI输出进行分类.
- 框架,使人工智能评估与临床用例保持一致.
结论:
- 清晰的分类学减少了生成AI幻觉评估中的模糊性.
- 标准化定义有助于利益相关者之间更好的沟通.
- 更好的理解提高了AI在放射学中的可靠性和实用性.
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