Abstract
This chapter investigates the integration of AI and machine learning (ML) techniques in medical assistive robotics, focusing on their potential in enhancing healthcare capabilities. It explores the synergy between AI, ML, and medical robotics; outlines the chosen methodology; and assesses AI applications in areas like image analysis, predictive modeling, real-time monitoring, surgical automation, and rehabilitation. The study compares results with existing literature, revealing insights into the contributions and limitations of AI-empowered medical robotics. The findings highlight the transformative possibilities of AI and ML in advancing patient care, diagnostics, and treatment planning. By bridging theoretical understanding with empirical validation, this chapter aims to advance the discourse on AI integration in medical assistive robotics.
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