Securing Smart Surgical Systems: The Role of AI and Machine Learning in Robotic Surgery

Abstract

Smart surgical systems and robotic-assisted procedures are reshaping modern healthcare by enhancing surgical precision, reducing invasiveness, and enabling data-driven clinical decision-making. These systems integrate surgical robots, imaging platforms, real-time sensor data, hospital networks, and cloud-based services, forming highly connected and software-intensive environments. While this connectivity improves clinical outcomes, it also expands the cyber attack surface, making security a critical factor directly linked to patient safety and system reliability. Artificial intelligence (AI) and machine learning (ML) are emerging as essential technologies for securing these next-generation surgical ecosystems.

This article examines the role of AI and ML in strengthening cybersecurity for robotic surgery and smart surgical systems. Machine learning techniques enable continuous monitoring of device behavior, network traffic, and user interactions to detect anomalies, identify unauthorized access, and mitigate cyber threats in real time. AI-driven security models support predictive risk assessment, adaptive access control, and automated incident response, aligning with zero trust and defense-in-depth security architectures tailored for safety-critical medical environments.

The paper explores architectural and operational considerations for deploying AI-enabled security in surgical systems, including edge-based inference for low-latency protection, secure data pipelines, model validation, and integration with clinical workflows. It also addresses challenges such as explainability, regulatory compliance, adversarial attacks on AI models, and the need for human oversight to maintain trust and accountability in autonomous security operations.

Through analysis of real-world use cases and emerging best practices, this article demonstrates how AI and ML enhance the resilience, integrity, and trustworthiness of smart surgical systems. It concludes that AI-driven cybersecurity is not only a technical necessity but a foundational requirement for the safe, scalable, and compliant adoption of robotic surgery in modern healthcare.