The aim of the current narrative review was to summarize the available evidence in the literature on artificial intelligence (AI) methods that have been applied during robotic surgery.
A narrative review of the literature was performed on MEDLINE/Pubmed and Scopus database on the topics of artificial intelligence, autonomous surgery, machine learning, robotic surgery, and surgical navigation, focusing on articles published between January 2015 and June 2019. All available evidences were analyzed and summarized herein after an interactive peer-review process of the panel.
The preliminary results of the implementation of AI in clinical setting are encouraging. By providing a readout of the full telemetry and a sophisticated viewing console, robot-assisted surgery can be used to study and refine the application of AI in surgical practice. Machine learning approaches strengthen the feedback regarding surgical skills acquisition, efficiency of the surgical process, surgical guidance and prediction of postoperative outcomes. Tension-sensors on the robotic arms and the integration of augmented reality methods can help enhance the surgical experience and monitor organ movements.
The use of AI in robotic surgery is expected to have a significant impact on future surgical training as well as enhance the surgical experience during a procedure. Both aim to realize precision surgery and thus to increase the quality of the surgical care. Implementation of AI in master–slave robotic surgery may allow for the careful, step-by-step consideration of autonomous robotic surgery.
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This research was conducted with the support of the European Urological Scholarship Programme and an NWO TTW VICI grant (TTW BTG 16141).
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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The authors declare that they have no conflicts of interest.
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Andras, I., Mazzone, E., van Leeuwen, F.W.B. et al. Artificial intelligence and robotics: a combination that is changing the operating room. World J Urol 38, 2359–2366 (2020). https://doi.org/10.1007/s00345-019-03037-6
- Artificial intelligence
- Autonomous surgery
- Machine learning
- Robotic surgery
- Surgical navigation