放射学中的对抗性人工智能:攻击,防御和未来考虑
Nicholas Dietrich1, Bo Gong2, Michael N Patlas3
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.
Diagnostic and interventional imaging
|May 22, 2025
概括
放射学中的人工智能 (AI) 容易受到对抗性攻击. 本综述探讨了这些风险,它们的临床影响,以及在医学成像中可靠的人工智能减轻风险的策略.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像安全 医疗成像安全
背景情况:
- 人工智能 (AI) 在放射学中越来越多地用于疾病检测和图像分析等任务.
- 人工智能在临床实践中的整合引发了人们对其易受敌对攻击的担忧.
- 了解这些漏洞对于确保AI在医疗保健中的可靠性至关重要.
研究的目的:
- 为放射学中对抗性AI提供全面的概述.
- 概述基础概念和模型特征,使人工智能易受操纵.
- 为了分类与放射学相关的不同类型的对抗性攻击.
主要方法:
- 对放射学中的对抗性AI现有文献的审查.
- 基于攻击者知识,目标,时间和频率的攻击类型的分类.
- 检查各种放射学任务的临床影响.
- 分析当前的缓解策略和保护方法.
主要成果:
- 放射学中的AI系统容易受到对抗性操纵的影响,影响疾病分类,细分和报告生成.
- 攻击带来风险,包括伤害患者,操作中断和侵蚀对人工智能系统的信任.
- 存在各种缓解策略,包括输入防御,修改训练和认证的强度.
结论:
- 敌对人工智能对放射学中人工智能的安全和有效部署构成重大风险.
- 强大的缓解策略和生命周期保护措施对于开发可信的人工智能至关重要.
- 需要进一步的研究来解决发现的差距,并优先考虑未来的发展.
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