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Artificial intelligence (AI) in radiology is vulnerable to adversarial attacks. This review explores these risks, their clinical impact, and mitigation strategies for trustworthy AI in medical imaging.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging Security

Background:

  • Artificial intelligence (AI) is increasingly used in radiology for tasks like disease detection and image analysis.
  • The integration of AI in clinical practice raises concerns about its susceptibility to adversarial attacks.
  • Understanding these vulnerabilities is crucial for ensuring the reliability of AI in healthcare.

Purpose of the Study:

  • To provide a comprehensive overview of adversarial AI in radiology.
  • To outline foundational concepts and model characteristics making AI susceptible to manipulation.
  • To categorize different types of adversarial attacks relevant to radiology.

Main Methods:

  • Review of existing literature on adversarial AI in radiology.
  • Classification of attack types based on attacker knowledge, goals, timing, and frequency.
  • Examination of clinical implications across various radiology tasks.
  • Analysis of current mitigation strategies and safeguard approaches.

Main Results:

  • AI systems in radiology are vulnerable to adversarial manipulation, impacting disease classification, segmentation, and report generation.
  • Attacks pose risks including patient harm, operational disruption, and erosion of trust in AI systems.
  • Various mitigation strategies exist, including input defenses, modified training, and certified robustness.

Conclusions:

  • Adversarial AI poses significant risks to the safe and effective deployment of AI in radiology.
  • Robust mitigation strategies and lifecycle safeguards are essential for developing trustworthy AI.
  • Further research is needed to address identified gaps and prioritize future development.