Cybersecurity considerations for radiology departments involved with artificial intelligence

Area of Science:

• Medical Imaging and Artificial Intelligence
• Cybersecurity in Healthcare

Background:

• The integration of medical devices, wireless technologies, and data warehouses in radiology AI projects has amplified cybersecurity risks.
• While radiologists excel at image interpretation, they may lack awareness of AI-specific cybersecurity threats, a growing concern in healthcare.

Purpose of the Study:

• To introduce cybersecurity concepts relevant to medical imaging and radiology AI.
• To provide background on general and healthcare-specific cybersecurity challenges.
• To discuss strategies for enhancing security through detection, prevention, and technological solutions.

Main Methods:

• Review of general cybersecurity concepts and regulatory issues.
• Examination of cybersecurity in the context of radiology AI, focusing on data, training, implementation, and auditability.
• Identification of potential risk mitigation strategies.

Main Results:

• Radiology AI projects face complex cybersecurity risks due to increased threat prevalence.
• Lessons can be learned from other industries that have advanced their cybersecurity measures.
• The review outlines detection and prevention techniques and technological solutions for improved security.

Conclusions:

• Healthcare providers, researchers, and device developers need a better understanding of radiology AI cybersecurity risks.
• Implementing effective cybersecurity strategies is crucial for mitigating risks in AI-driven radiology.
• This review offers insights to improve security and reduce potential risks associated with radiology AI projects.