Smart Surgical Assistance: Integrating Reinforcement Learning for Optimized Decision-Making in Minimally Invasive Procedures

Abstract

An increasing use of artificial intelligence (AI) technologies, especially reinforcement learning (RL) systems, is quickly changing the way surgeries are done. This article describes a new way to use RL to help surgeons make better decisions during minimally invasive surgeries. We look into how to create and use an RL system that will help doctors by giving them real-time, data-driven insights and suggestions. The goal is to make surgery procedures more accurate and effective. As part of the study, a training setting that looks and feels like a real surgery was created. This lets the RL model learn and improve its tactics by making mistakes and getting feedback and making changes all the time. Key results show that the RL model makes it much easier to make decisions when there is doubt, which leads to more exact movements and shorter operating times. One big improvement is that the system can change to changing surgery settings and make decision paths that are unique for each patient. Also, putting RL into the operating room has shown promise in making it easier for doctors to think, which could possibly lower the number of mistakes they make. There is talk about ethical issues, how feasible it is to add these kinds of systems to current medical infrastructure, and the long-term effects on surgery training and results. This study shows that RL has the ability to change the field of surgery by giving doctors smart, flexible, and accurate tools for making decisions.