A survey of machine learning techniques in advanced robotics in surgical applications

The advent of a new age of innovation and accuracy in surgical applications has been ushered in with the combination of methods for machine learning and cutting-edge robotic systems. The purpose of this review article is to offer a complete analysis of the synergistic link that exists between sophisticated robotics and machine learning in the field of surgery. We give a comprehensive examination of a variety of machine learning algorithms and their application in the expansion of the capabilities of surgical robots, which will lead to a revolution in the area of minimally invasive as well as difficult procedures. This study studies the use of several machine learning paradigms, such as supervised, unsupervised, and reinforcement learning, in the field of surgical robotics. The scope of the study spans a wide variety of machine learning paradigms. We dig into the complexities of data-driven approaches, including image analysis, gesture recognition, and sensor fusion, and emphasize the critical roles that these methods play in allowing robots to interpret, traverse, and interact with the surgical environment.