Cybersecurity considerations for radiology departments involved with artificial intelligence

Radiology artificial intelligence (AI) projects involve the integration of integrating numerous medical devices, wireless technologies, data warehouses, and social networks. While cybersecurity threats are not new to healthcare, their prevalence has increased with the rise of AI research for applications in radiology, making them one of the major healthcare risks of 2021. Radiologists have extensive experience with the interpretation of medical imaging data but radiologists may not have the required level of awareness or training related to AI-specific cybersecurity concerns. Healthcare providers and device manufacturers can learn from other industry sector industries that have already taken steps to improve their cybersecurity systems. This review aims to introduce cybersecurity concepts as it relates to medical imaging and to provide background information on general and healthcare-specific cybersecurity challenges. We discuss approaches to enhancing the level and effectiveness of security through detection and prevention techniques, as well as ways that technology can improve security while mitigating risks. We first review general cybersecurity concepts and regulatory issues before examining these topics in the context of radiology AI, with a specific focus on data, training, data, training, implementation, and auditability. Finally, we suggest potential risk mitigation strategies. By reading this review, healthcare providers, researchers, and device developers can gain a better understanding of the potential risks associated with radiology AI projects, as well as strategies to improve cybersecurity and reduce potential associated risks. [GRAPHICS] .

Automated summary

Radiology AI projects involve integrating multiple medical devices, wireless technologies, data warehouses, and social networks. The rise of AI research in radiology has increased cybersecurity threats in healthcare, making them a major risk in 2021. This review provides an introduction to cybersecurity concepts in medical imaging and discusses approaches to enhance security through detection and prevention techniques. It also suggests potential risk mitigation strategies for radiology AI projects.