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A systematic review on artificial intelligence in robot-assisted surgery.

Andrea Moglia¹, Konstantinos Georgiou, Evangelos Georgiou

¹EndoCAS, Center for Computer Assisted Surgery, University of Pisa, 56124, Pisa, Italy 1st Propaedeutic Surgical Unit, Hippocrateion Athens General Hospital, Athens Medical School, National and Kapodistrian University of Athens, Greece MPLSC, Athens Medical School, National and Kapodistrian University of Athens, Greece Department of Surgery, University of Washington Medical Center, Seattle, WA, United States Scuola Superiore Sant'Anna of Pisa, 56214, Pisa, Italy Institute for Medical Science and Technology, University of Dundee, Dundee, DD2 1FD, United Kingdom.

International Journal of Surgery (London, England)

|October 25, 2021

Summary

Artificial intelligence (AI) shows potential in robot-assisted surgery (RAS), but current studies lack quality and large datasets. More research is needed to prove AI

Keywords:

Artificial intelligence robot-assisted surgery Artificial intelligence robotic surgery Computer vision robotic surgery Deep learning robot-assisted surgery General surgery robot surgery Machine learning robot-assisted surgery Urology robotic surgery

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Area of Science:

Robotics and Artificial Intelligence in Surgery
Surgical Patient Safety
Medical Technology Assessment

Background:

Limited research exists on artificial intelligence (AI) efficacy in enhancing patient safety within robot-assisted surgery (RAS).
A systematic review was conducted to address this gap, identifying current challenges and limitations.

Purpose of the Study:

To systematically review the literature on AI applications in robot-assisted surgery (RAS).
To identify and discuss the limitations and challenges of current AI in RAS.

Main Methods:

A comprehensive literature search was performed across PubMed, Web of Science, Scopus, and IEEExplore (2016-2020).
Studies were assessed for quality using Amstar 2 and risk of bias with the Newcastle Ottawa Quality assessment tool.
Data were synthesized and presented using the SPIDER tool.

Main Results:

Thirty-five publications involving 3436 patients were analyzed, focusing on motion analysis, urology, gynecology, and training.
AI demonstrated varying precision in surgical tool detection (76.0%-90.6%) and accuracy in predicting outcomes like urinary continence and length of stay post-prostatectomy.
Accuracy in recognizing surgical tasks during partial nephrectomy was 75.7%.

Conclusions:

The reviewed studies were of low quality, with small datasets and significant heterogeneity.
Current AI cannot reliably identify critical RAS tasks impacting patient outcomes.
There is an urgent need for large-scale studies, external validation, and transparent AI results for surgeons.