Security analysis and recommendations for AI/ML enabled automated cyber medical systems

Abstract

Artificial Intelligence (AI) and remote surgery go together in defining the future of intelligent medicine. The advancement of robotic surgery became a possibility by leveraging intelligent sensors, Machine Learning (ML), and reliable wireless connectivity in addition to low-latency response to surgical commands and automated responses to patient's status resulting in an accurate automated system. The trend is to develop cyber-medical systems through the integration of medical knowledge and engineering applications in order to create customizable intelligent healthcare procedures. Security plays a critical role while the systems navigate the dynamics of human-controlled connectivity versus autonomy, and remote commands versus automated responses to sensors. The systems need to “learn” from experience and transferred knowledge from other systems while protecting against learning from false data. This paper addresses the Cyber Security risks introduced by adapting the emerging technologies as well as providing potential solutions that are based on best practices. The ML model used in cyber medicine should be as simple as possible providing the required accuracy to produce the desired outcome, understanding that more complex models have higher chances of suffering the degradation effects of overfitting.