Iulia Andras1,2, Elio Mazzone1,3,4, Fijs W B van Leeuwen1,5,6, Geert De Naeyer1,3, Matthias N van Oosterom5,6, Sergi Beato1, Tessa Buckle5, Shane O'Sullivan7, Pim J van Leeuwen6, Alexander Beulens8,9, Nicolae Crisan2, Frederiek D'Hondt1,3, Peter Schatteman1,3, Henk van Der Poel6, Paolo Dell'Oglio10,11,12,13, Alexandre Mottrie1,3. 1. ORSI Academy, Melle, Belgium. 2. Department of Urology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania. 3. Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium. 4. Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy. 5. Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands. 6. Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands. 7. Department of Pathology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. 8. Department of Urology, Catharina Hospital, Eindhoven, The Netherlands. 9. Netherlands Institute for Health Services (NIVEL), Utrecht, The Netherlands. 10. ORSI Academy, Melle, Belgium. paolo.delloglio@gmail.com. 11. Department of Urology, Onze Lieve Vrouw Hospital, Aalst, Belgium. paolo.delloglio@gmail.com. 12. Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands. paolo.delloglio@gmail.com. 13. Department of Urology, Antoni Van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands. paolo.delloglio@gmail.com.
Abstract
PURPOSE: 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. METHODS: 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. LITERATURE REVIEW: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. LITERATURE REVIEW: 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. CONCLUSIONS: 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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