Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg 2021; 13(1): 7-18 [PMID: 33552391 DOI: 10.4240/wjgs.v13.i1.7]
Corresponding Author of This Article
Bobby V M Dasari, MS, FRCS, Surgeon, Consultant HPB and Liver Transplant Surgeon, Department of Liver Surgery, Queen Elizabeth Hospital, Edgbaston, Birmingham B15 2TH, United Kingdom. bobby.dasari@uhb.nhs.uk
Research Domain of This Article
Surgery
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Surg. Jan 27, 2021; 13(1): 7-18 Published online Jan 27, 2021. doi: 10.4240/wjgs.v13.i1.7
Role of artificial intelligence in hepatobiliary and pancreatic surgery
Hassaan Bari, Sharan Wadhwani, Bobby V M Dasari
Hassaan Bari, Bobby V M Dasari, Department of HPB and Liver Transplantation Surgery, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
Sharan Wadhwani, Department of Radiology, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
Author contributions: Dasari BVM designed the study; Bari H and Dasari BVM performed the literature search and analyzed the literature; Dasari BVM and Wadhwani S provided expert input; Bari H, Dasari BVM and Wadhwani S wrote the manuscript; all authors have read and approve the final manuscript.
Conflict-of-interest statement: There are no conflicts of interest to declare from all the three authors.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bobby V M Dasari, MS, FRCS, Surgeon, Consultant HPB and Liver Transplant Surgeon, Department of Liver Surgery, Queen Elizabeth Hospital, Edgbaston, Birmingham B15 2TH, United Kingdom. bobby.dasari@uhb.nhs.uk
Received: October 28, 2020 Peer-review started: October 28, 2020 First decision: November 30, 2020 Revised: December 8, 2020 Accepted: December 17, 2020 Article in press: December 17, 2020 Published online: January 27, 2021
Abstract
Over the past decade, enhanced preoperative imaging and visualization, improved delineation of the complex anatomical structures of the liver and pancreas, and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes. Artificial intelligence (AI) has a major role to play in 3D visualization, virtual simulation, augmented reality that helps in the training of surgeons and the future delivery of conventional, laparoscopic, and robotic hepatobiliary and pancreatic (HPB) surgery; artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging, and postoperative surveillance. In this paper, we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.
Core Tip: The use of artificial intelligence (AI) increases hepatobiliary surgeons' capability in the timely selection of appropriate patients for precise, personalized delivery of complex surgical procedures with increased safety and ease. Published studies have mainly concentrated on assessing the technical feasibility of utilizing AI, and future research needs to focus on delivering and assessing the clinical impact of these promising techniques.
