Abstract

As healthcare delivery becomes increasingly dependent on AI-driven analytics and robotic systems, ensuring the confidentiality, integrity, and availability of patients’ data and automated processes is paramount. This paper surveys the state of the art in security architectures tailored for intelligent and robotic healthcare platforms. It reviews emerging frameworks - Zero Trust, DevSecOps, federated learning, and hardware-rooted trust - and distills best practices in threat modeling, cryptographic data management, network segmentation, and secure development lifecycles. It also illustrates how these approaches interoperate to build layered, resilient defenses against sophisticated cyber-physical threats, compliance gaps, and supply-chain risks. A matrix of frameworks versus capabilities guides practitioners in selecting and integrating security controls that meet both technical and regulatory requirements. Finally, the paper outlines open research challenges and proposes a roadmap for future advances in secure and trustworthy AI and robotics in healthcare.

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