Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review

Themistoklis Bellos ¹, Ioannis Manolitsis ¹, Stamatios Katsimperis ¹

⁰2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece.

Cancers +

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May 11, 2024

View abstract on PubMed

Summary

Machine learning and artificial intelligence show promise in robotic urologic surgery, enhancing precision and improving patient outcomes. These technologies aid in surgical training, guidance, and prediction, advancing the field of automated surgical procedures.

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.

Keywords:

artificial intelligence machine learning robotic surgery urologic surgery