The field of surgery has undergone a revolutionary transformation with the integration of robotic-assisted surgery (RAS). Robotic-assisted surgical systems have significantly enhanced precision, efficiency, and patient outcomes. Traditional robotic-assisted surgical systems operate within a “master-slave” dynamic, where human surgeons maintain complete control over the robotic instruments. The best current example is the da Vinci Surgical System from Sunnyvale, Calif.-based Intuitive Surgical, in which a human performs all surgical gestures in a console setting and the gestures are transmitted to robotic arms docked in the patient surgical site. Basically, the da Vinci robot cannot act autonomously without human input.

While these systems offer significant advantages, they also introduce notable cybersecurity concerns. Traditional robotic systems rely on interconnected networks, control consoles, and data transmission channels that can be targeted by cyber attackers. The growing reliance on such technologies makes them susceptible to data breaches, unauthorized access, and system manipulation, which could have direct consequences on patient safety and surgical outcomes.

Another complementing technology to robotic surgical systems is cloud intelligence. Cloud computing plays a crucial role in enhancing robotic surgeries by enabling real-time data processing, remote surgical assistance, and advanced data analytics. Through cloud intelligence, robotic surgery systems can store and process large amounts of surgical data, improve decision-making, and provide predictive analytics for surgeons. Cloud-based platforms also facilitate remote collaboration, allowing surgeons to access real-time insights and even perform telesurgery with minimal latency. However, the integration of cloud intelligence introduces new cybersecurity risks around interoperability and data integrity, including potential data breaches, unauthorized access to cloud-stored surgical data, and vulnerabilities in data transmission pathways. Cyber attackers could exploit these vulnerabilities to intercept data, disrupt surgical procedures, or gain unauthorized control over robotic systems. Implementing robust encryption, secure authentication mechanisms, and continuous monitoring is essential to safeguard these cloud-based systems.

Robotic surgical systems face a wide range of cybersecurity threats that can impact patient safety and operational continuity. Data breaches also pose significant privacy concerns, as robotic-assisted surgeries generate and transmit sensitive patient data, including imaging, biometrics, and procedural telemetry. Unauthorized access to this data could result in privacy violations and compromise patient confidentiality. Additionally, attackers may manipulate control systems or exploit network vulnerabilities, potentially altering surgical outcomes and posing direct risks to patient safety. System manipulation, ransomware attacks, and remote exploits further heighten these threats, emphasizing the need for robust security measures.

System manipulation can result in the alteration of robotic commands, leading to unintended surgical movements that may harm patients or compromise procedural precision. Such tampering can disrupt the clinical functionality of surgical robots, posing direct risks to patient safety and surgical outcomes. Ransomware attacks can lock critical surgical systems, making them inaccessible until a ransom is paid. In a surgical context, this can lead to the cancellation or delay of life-saving procedures, severely affecting patient care and hospital operations. Moreover, remote exploits—where attackers infiltrate systems via network vulnerabilities—can grant unauthorized access to control interfaces, enabling malicious actors to disrupt or take control of robotic surgical procedures. These risks not only endanger patients but also undermine the availability and reliability of robotic systems, crucial factors in maintaining consistent clinical functionality.

Cloud integration, while offering numerous advantages, also introduces specific cybersecurity vulnerabilities that expand the attack surface. Data interception during transmission, unauthorized access to cloud resources, and denial of service (DoS) attacks are all potential threats that can disrupt surgical procedures. The multi-tenant nature of cloud platforms increases exposure to shared infrastructure risks, where attackers could exploit vulnerabilities in one system to access others. Additionally, data loss or corruption resulting from service failures or targeted attacks could impact surgical planning. 

To further complicate the cybersecurity landscape, the field of surgery is undergoing revolutionary transformation with the integration of artificial intelligence (AI). AI-enabled surgical systems, particularly RAS, have significantly enhanced precision, efficiency, and patient outcomes by analyzing vast amounts of real-time data during procedures and optimized surgical pathways. AI innovations in robotic surgery are largely driven by intraoperative enhancements that optimize surgical performance. Broadly, these enhancements fall into two key categories:

• Robotic Autonomy: AI is enabling increased levels of automation in surgical procedures. While fully autonomous robotic surgery is not yet a reality, AI-powered robots are being developed to assist with complex tasks such as tissue manipulation, suturing, and real-time decision-making based on intraoperative imaging.
• Surgical Assessment and Feedback: AI is also playing a critical role in improving surgical outcomes by providing intraoperative feedback to surgeons. AI models can analyze live surgical video feeds to provide insights on technique optimization, anatomical recognition, and predictive analytics for better decision-making.

One example of such an innovation is Holo AI, an advanced technology platform that combines AI with augmented reality (AR) to enhance surgical visualization and precision. Holo AI allows surgeons to interact with a digital overlay of patient anatomy in real time, providing more accurate guidance during procedures. The use of AR and AI together improves spatial awareness, reduces errors, and enhances surgical efficiency. However, this increased level of interconnectivity and reliance on real-time data also exposes AI surgical systems to new cybersecurity threats. Another notable AI-enabled surgical system is Aprevo AI, which is used in spinal surgery. Aprevo AI leverages machine learning algorithms to optimize preoperative planning and implant selection, ensuring patient-specific surgical precision. 

This shift is largely driven by AI’s increasing ability to process vast amounts of data collected through imaging, sensor fusion, and real-time analysis. However, this integration of AI also introduces complex cybersecurity challenges that traditional systems did not encounter.

As AI technology begins to evolve into machine learning in surgical systems, it will particularly be vulnerable to data poisoning attacks, where malicious actors manipulate the data used to train AI algorithms, leading to skewed or dangerous outputs during critical procedures. Adversarial attacks, where input data is subtly altered to mislead AI models, pose significant risks in real-time surgical environments. The complexity of AI algorithms makes it difficult to identify and mitigate such threats promptly, increasing the potential impact of cyber incidents. Furthermore, the interconnected ecosystems—where AI-enabled surgical robots continuously exchange data with cloud servers, electronic health records, and other medical devices—broaden the attack surface for cybercriminals. Unauthorized access to AI algorithms or the introduction of malicious code can compromise entire surgical workflows, leading to data breaches or system failures.

Mitigating Cybersecurity Risks in Robotic Surgical Systems 

To effectively safeguard robotic surgical systems, a multi-layered proactive cybersecurity approach is essential in both pre-market and post market phases of design and development. This includes threat modelling, security risk assessments, robust and deliberate security architecture built for interoperability. In complex surgical systems, third-party components are integral to design and development, making SBOMs crucial for tracking vulnerabilities continuously. In addition, robust data encryption, strict access controls, and regular patch management help secure patient data and strengthen overall system resilience. U.S. Food and Drug Administration guidance also focuses on adopting a zero-trust security architecture, enforcing strong authentication protocols, and continuous system monitoring to further reduce exposure to threats. Cloud-specific measures—such as encrypting data during transmission, using secure APIs (application programming interfaces), and regular audits—ensure cloud integrity. In the post-market phase, establishing strong incident response plans and conducting regular penetration tests ensure operational continuity and patient safety.

Conclusion 

Cybersecurity is a shared responsibility among medical device manufacturers, healthcare delivery organizations, and surgeons to ensure availability, integrity, and clinical functionality. Continuous availability prevents life-threatening disruptions, while data integrity protects against manipulation and unauthorized access. Cybersecurity must be baked into design and deployment, particularly as AI and cloud integration expand, proactive risk management, continuous monitoring and strong security protocols can help build secure-by-design devices. I am excited to see the transformative potential of robotic-assisted surgery fully realized hopefully in this era!

Nidhi Gani is the regulatory cybersecurity lead at MCRA. In this role she is responsible for regulatory cybersecurity strategy for medical devices across diverse therapies like neurology, orthopedics, diabetes management, cardiovascular, and radiology. Before MCRA, she worked for fortune 500 companies and startups for eight years. Gani is also an adjunct faculty member at Northeastern University who was the first person to teach digital health and cybersecurity. She is also the cybersecurity fellow at Archimedes Center for Medical Device and Healthcare Security with a bachelor’s degree in biotechnology engineering and a master’s degree in regulatory affairs.