明显的视觉偏见影响人类和人工智能在医学成像诊断中的诊断
Graham A McLeod1,2, Emma A M Stanley3,4,5,6, Tom Rosenal7,8,9
1Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, USA. gmcleod@stanford.edu.
NPJ digital medicine
|December 22, 2025
概括
人工智能 (AI) 可以在医学扫描中找到微妙的模式,以便更早地检测疾病. 然而,人工智能 (AI) 的
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 放射学和诊断成像学 放射学和诊断成像学
背景情况:
- 人工智能 (AI) 系统擅长识别诊断成像中的微妙特征,可能超过人类放射科医生.
- 这些AI能力可以促进早期的疾病检测和非侵入性病变特征.
研究的目的:
- 探索放射科医生和人工智能在医学图像中感知细节的不同方式.
- 了解这些感知差异对医疗决策的含义.
- 强调与人工智能相关的风险,包括偏见和缺乏因果推理.
主要方法:
- 人工智能和放射科医生图像解释过程的比较分析.
- 探索AI依赖于相关性与因果关系的探索.
- 检查人工智能算法的潜在人口和技术偏差.
- 讨论AI推理中的解释性挑战.
主要成果:
- 人工智能系统检测到微妙和更高阶的成像特征,这些特征可能被放射科医生错过.
- 人工智能依赖于相关性,偏见和无法解释的推理构成风险.
- 人工智能和放射学家如何学习解释医疗图像存在显著差异.
结论:
- 人工智能为早期疾病检测和表型定型提供了潜在的好处,但需要仔细验证.
- 了解人工智能和放射科医生的独特感知机制对于安全整合至关重要.
- 解决人工智能偏见和确保可解释性是医学成像临床采用的关键挑战.
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