In recent years, Artificial Intelligence has experienced significant growth thanks to technological advances such as high performance computing and massive data processing. This evolution has led to a new reflection as well as a serious interest on the part of managers of hospital structures, who are beginning to take an interest in the automation and optimization of surgical processes. Machine Learning finds its place as a preferred technique for developing intelligent decision support systems in the operating room. In this aisle, it has become crucial to automate and optimize preoperative procedures. Through this paper, we focus on the development of an intelligent decision support system for the surgical preoperative phase, using the Random Forest model, which is an extension of the decision tree algorithm, to analyze a variety of preoperative predictive data such as hypertension, body temperature, ECG, hemoglobin, etc. We use a dataset previously approved by an institutional review board, while validating our model in a development environment dedicated to the field of Artificial Intelligence.

Background: Precision in catheter guidance is essential for the success of vascular surgeries, yet current methods often need more accuracy due to the complex anatomy and dynamics of blood vessels. Methods: This study evaluates the efficacy of advanced reinforcement learning (RL) techniques to enhance catheter navigation. We compare different RL approaches within simulated vascular environments, focusing on their success rates, operational efficiency, and adaptability to varied clinical scenarios. Results: Advanced reinforcement learning techniques display exceptional performance, yielding high success rates and improved precision in catheter guidance. Integrating specific enhancements has notably increased learning speeds and strengthened operational robustness. Conclusion: The study indicates that reinforcement learning could significantly improve the precision and safety of catheter navigation in vascular surgery. By adopting these techniques, medical practices could see more accurate and less invasive procedures, enhancing patient outcomes. Future research should aim to refine these algorithms for wider clinical use and integration.

Problem: The precision, efficiency, and safety of surgical procedures need significant improvements. Traditional methods are limited by human capabilities, and existing robotic systems lack the advanced adaptability required for complex surgical tasks. The integration of reinforcement learning (RL) into robotic surgery represents a potential revolution in the medical field. Methods: This comparative review synthesizes recent progress in RL applications for robotic surgery. It highlights innovative methodologies and successful applications of RL, focusing on advanced simulations to train RL agents and the importance of human demonstrations in the learning process. Results: Emerging trends such as the effective use of simulations and human demonstrations to enhance RL in robotic surgery are identified. The review also discusses challenges associated with RL applications, emphasizing the need for clinical validation and ensuring patient safety. Conclusion: The transformative potential of RL in robotic surgery is evident, though challenges remain. Future work should prioritize clinical validation, patient safety, and interdisciplinary collaboration.