Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review.

Area of Science:

• Urologic Surgery
• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Machine learning (ML) applications are expanding across various sectors, including therapeutic settings.
• Robotic surgery offers precise telemetry data and advanced visualization for AI integration.
• The increasing capacity of computer processing fuels advancements in AI and ML.

Purpose of the Study:

• To analyze current studies on machine learning in robotic urologic surgery.
• To identify key areas of focus and promising outcomes of AI in this surgical domain.
• To explore the potential of AI and ML to enhance surgical training, guidance, and prediction.

Main Methods:

• Searched PubMed/Medline and Google Scholar databases up to December 2023.
• Utilized search terms including "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery".
• Focused on studies related to automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction.

Main Results:

• Early therapeutic outcomes of artificial intelligence (AI) in urologic surgery are promising.
• Key focuses include automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction.
• ML enhances surgical skill feedback, procedure effectiveness, guidance, and postoperative prediction.

Conclusions:

• AI in robotic surgery aims to improve surgical training and patient care through enhanced precision.
• Integration of technologies like tension sensors and augmented reality can further improve surgical accuracy.
• Advancements in datasets and electronic health records will increase the effectiveness and utility of these AI technologies.