放射学中的对抗性攻击 - - 一个系统的审查.
Vera Sorin1, Shelly Soffer2, Benjamin S Glicksberg3
1Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
对放射学深度学习的敌对攻击是越来越严重的网络安全问题. 虽然目前是理论上的,但这些攻击构成风险,需要加强安全性和安全AI实施的伦理准则.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 医疗保健中的网络安全
- 深度学习在放射学中的应用.
背景情况:
- 深度学习在放射学中越来越多地使用,这引发了网络安全的担忧,特别是关于对抗性攻击.
- 敌对攻击可以操纵人工智能模型,可能会影响诊断准确性和患者安全.
- 需要进行系统审查,以了解现场对抗性攻击的情况.
结论:
- 敌对攻击对放射学深度学习的安全部署构成重大风险,尽管目前只是理论上的风险.
- 积极的措施,包括加强网络安全协议和制定道德和法律框架,至关重要.
- 确保在医学成像中安全可靠地使用人工智能,需要持续保持警和适应.
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