Cybersecurity in Medical Robotics

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,

Robotic systems grow more in medical procedures, it is critical to ensure their cybersecurity to protect patient information, preserve system integrity in medical technology.

1. Patient privacy and data encryption

Strong encryption procedures must be compulsory to protect patient data that medical robotic systems transmit and store. Protecting patient privacy and confidentiality is made possible by ensuring end-to-end encryption in communication channels [1].

2. Protocols for Secure Communication

Communication networks are frequently used by medical robots to provide and receive data and commands. Using secure communication protocols, like VPNs or HTTPS, helps guard against unwanted control of robotic systems [2] .

3. Consistent Patch Management and Software Updates

To fix vulnerabilities, medical robotic systems should have regular software updates and patch management [3].

4. Authentication and Access Control

It is essential to provide strong user authentication procedures and strict access limits. Medical robotic systems should only be accessible by authorized individuals, and multi-factor authentication provides an additional degree of protection. This lessens the possibility of unauthorised people controlling or tampering with the robots [4].

5. Firmware integrity and secure boot

Preventing unwanted changes to the robotic system’s software necessitates making sure the safe boot procedure is followed and confirming the integrity of the firmware. The overall security posture is improved by secure boot procedures, which ensure that only authenticated and unaltered firmware can be performed [5].

6. Awareness and Training of Users

Cybersecurity is significantly impacted by human factors. A proactive defence against social engineering attacks and unintentional security gaps includes educating healthcare professionals and operators about potential risks and dangers and providing them with cybersecurity best practices training.

7. Adherence to Regulations

Respecting cybersecurity laws and guidelines is essential in the field of medical robots. Adherence to regulatory frameworks like the Health Insurance Portability and Accountability Act (HIPAA) guarantees robotic systems conform to cybersecurity standards and norms that are relevant to the industry [6].

When technology and healthcare are combining, it is not only technically but also in tackling cybersecurity issues. To fully utilise medical robotics and protect patient safety and trust, the healthcare sector needs to be alert, proactive, and cooperative.


  1. Shaikh, T. A., Rasool, T., & Verma, P. (2023). Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions. Artificial Intelligence in Medicine, 102692.3
  2. Bhushan, B., Kumar, A., Agarwal, A. K., Kumar, A., Bhattacharya, P., & Kumar, A. (2023). Towards a Secure and Sustainable Internet of Medical Things (IoMT): Requirements, Design Challenges, Security Techniques, and Future Trends. Sustainability, 15(7), 6177.
  3. Stasevych, M., & Zvarych, V. (2023). Innovative robotic technologies and artificial intelligence in pharmacy and medicine: paving the way for the future of health care—a review. Big Data and Cognitive Computing, 7(3), 147.
  4. Tahseen, A. J. A., Hani, A., Lyashenko, V., Ayman, A., Sotnik, S., & Ahmed, S. (2023). Access control to robotic systems based on biometric: the generalized model and its practical implementation.
  5. Oruma, S. O., & Petrović, S. (2023). Security Threats to 5G Networks for Social Robots in Public Spaces: A Survey. IEEE Access.
  6. Evans, B. J. (2023). Rules for robots, and why medical AI breaks them. Journal of Law and the Biosciences, 10(1), lsad001.
  7. Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022). An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Transactions on Industrial Informatics, 18(11), 7946-7954.
  8. Liu, R. W., Guo, Y., Lu, Y., Chui, K. T., & Gupta, B. B. (2022). Deep network-enabled haze visibility enhancement for visual IoT-driven intelligent transportation systems. IEEE Transactions on Industrial Informatics, 19(2), 1581-1591.
  9. Lu, J., Shen, J., Vijayakumar, P., & Gupta, B. B. (2021). Blockchain-based secure data storage protocol for sensors in the industrial internet of things. IEEE Transactions on Industrial Informatics, 18(8), 5422-5431.
  10. Xu, M., Peng, J., Gupta, B. B., Kang, J., Xiong, Z., Li, Z., & Abd El-Latif, A. A. (2021). Multiagent federated reinforcement learning for Secure Incentive Mechanism in Intelligent Cyber–Physical Systems. IEEE Internet of Things Journal, 9(22), 22095-22108.

Cite As:

Vajrobol V. (2024) Cybersecurity in Medical Robotics, Insights2Techinfo, pp.1

68410cookie-checkCybersecurity in Medical Robotics
Share this:

Leave a Reply

Your email address will not be published.