DOI: https://doi.org/10.36347/sjet.2025.v13i11.002

Pages: 860-869

The convergence of artificial intelligence with medical robotics poses resilience and security challenges unique for embedded systems. In this thorough review, considerations of architecture, mechanisms for protection, and strategies for resilience are examined. This review stresses the core needs for medical robots using AI within critical healthcare settings. We examine multidimensional security methods featuring encryption using hardware, advanced authentication systems, and continuing integrity checks to protect patient data plus operational tasks. More investigation of resilience engineering methods appears within the article. These methodologies incorporate redundant hardware architectures as well as self-healing mechanisms, and they manage adaptive power to ensure uninterrupted operation during component failures or resource constraints. AI integration introduces additional complexities since it requires specialized validation approaches, model protection frameworks, and explainability mechanisms that satisfy both clinical and regulatory requirements. By way of security and resilience strategies, next-generation medical robotics can achieve extraordinary reliability implementing them across hardware, software, and system architecture levels. This reliability is needed for some critical healthcare applications and does help to maintain regulatory compliance as well as to establish appropriate clinical trust.