ML Integration In Robotic-Assisted Surgical Devices: Enhancing Precision And Reducing Human Error

Abstract

The introduction of machine learning (ML) into robotic-assisted surgical machines is transforming modern surgery by adding more accuracy to it and reducing human error. Compared to traditional robotic systems, which offer mechanical stability and dexterity but lack cognitive support, machine learning (ML) introduces a transformative advantage. ML enables the system to analyse medical images, predict motion, and optimise workflow processes. These capabilities are grounded in data-driven analysis, allowing for more intelligent and efficient decision-making during robotic procedures. These characteristics allow robots to reason about complex anatomy, personalize surgical routes, and avoid risk during surgery. ML also helps the surgeons with predictive analytics, anomaly detection, and real-time decision aids in situations of high pressure. This paper introduces the idea of implementing ML in robotic surgery and compares it to the conventional and intelligent systems, algorithmic approaches to increased precision, and the effects on clinical safety. ML-based robotics is a fresh start in the sphere of surgery despite all the challenges related to the generalisation of data, ethics, and regulation.