Abstract

The integration of artificial intelligence into robotic surgery and smart prosthetic systems has redefined the landscape of precision medicine and personalized rehabilitation. AI-driven robotic platforms enable highly accurate and minimally invasive surgical procedures through advanced control algorithms, real-time imaging analysis, and adaptive motion planning. Concurrently, smart prosthetics embedded with machine learning capabilities and multimodal sensors are transforming rehabilitative care by offering responsive, user-specific mobility solutions, the long-term performance of these systems in real-world environments is constrained by challenges such as sensor drift, power limitations, feedback latency, and adaptability to dynamic user needs. Contextaware algorithms, self-diagnostic architectures, and lifelong learning models are increasingly being explored to enhance system resilience, autonomy, and personalization. The smart energy management and seamless human-prosthetic interfaces are essential for sustained device usability. This chapter presents a comprehensive analysis of the technological foundations, performance challenges, and future directions of AI-enabled robotic surgery and intelligent prosthetic devices. It outlines key innovations in real-time biofeedback, adaptive control, and energy optimization, while identifying current research gaps and proposing pathways for clinical scalability. The convergence of AI, biomedical engineering, and user-centered design holds the potential to elevate the standards of medical intervention and post-operative rehabilitation, thereby improving quality of life and clinical outcomes across diverse patient populations.

Introduction

The convergence of artificial intelligence (AI) and biomedical engineering has catalyzed groundbreaking developments in medical robotics and prosthetic technologies [1]. AI-powered robotic surgery systems have emerged as a transformative solution for enhancing surgical precision, reducing intraoperative risks, and facilitating minimally invasive procedures [2]. These systems integrate real-time imaging, sensor feedback, and intelligent control algorithms to assist surgeons in executing complex procedures with high levels of accuracy and consistency [3]. The automation and augmentation provided by such platforms reduce human error and enable standardized surgical outcomes [4]. Simultaneously, smart prosthetics embedded with AI-driven components are revolutionizing rehabilitation by offering personalized mobility solutions tailored to each user's biomechanics and neurological patterns. These advancements are reshaping the scope and delivery of modern medical care by fostering greater precision, autonomy, and adaptability in treatment strategies [5]. As robotic surgery systems become more advanced, their ability to operate semi-autonomously, interpret diagnostic imaging, and adjust to intraoperative variations is significantly improving [6]. These platforms leverage machine learning models to predict complications, optimize incision trajectories, and monitor patient vitals in real time [7]. AI enables robotic arms to perform highdexterity tasks, such as micro-suturing or navigating around delicate anatomical structures, which would otherwise be highly challenging for human hands alone [8]. In clinical practice, these capabilities reduce patient trauma, minimize recovery times, and improve post-operative outcomes. On the other hand, smart prosthetics are integrating deep learning algorithms and multimodal sensor systems to detect user intent, decode neuromuscular signals, and adapt movement control accordingly [9]. These systems represent a shift from passive prosthetic components to active, intelligent devices capable of real-time personalization and performance enhancement [10].