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World J Gastroenterol. Jun 7, 2021; 27(21): 2758-2770
Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2758
Artificial intelligence in perioperative management of major gastrointestinal surgeries
Sohan Lal Solanki, Saneya Pandrowala, Abhirup Nayak, Manish Bhandare, Reshma P Ambulkar, Shailesh V Shrikhande
Sohan Lal Solanki, Abhirup Nayak, Reshma P Ambulkar, Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
Saneya Pandrowala, Manish Bhandare, Shailesh V Shrikhande, Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
Author contributions: Solanki SL contributed idea of writing, literature search, scientific writing and manuscript writing, editing and final approval; Pandrowala S contributed literature search, scientific writing and manuscript writing, editing and final approval; Nayak A contributed literature search, manuscript editing, and final approval; Bhandare M, Ambulkar RP and Shrikhande SV contributed manuscript editing, proofreading and final approval.
Conflict-of-interest statement: The authors declare that they have no competing interests.
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: Sohan Lal Solanki, MD, Professor, Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, E Borges Marg, Parel, Mumbai 400012, Maharashtra, India. me_sohans@yahoo.co.in
Received: January 16, 2021
Peer-review started: January 16, 2021
First decision: March 29, 2021
Revised: April 6, 2021
Accepted: April 28, 2021
Article in press: April 28, 2021
Published online: June 7, 2021
Processing time: 130 Days and 17.3 Hours
Abstract

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.

Keywords: Algorithms; Artificial intelligence; Big data; Data management; Machine learning; Perioperative care

Core Tip: Artificial intelligence (AI) has revolutionized the way surgery and anesthesia are taught and practiced. Applications of AI in anesthesia are risk prediction, control of anesthesia by closed-loop anesthesia delivery systems, monitoring the depth of anesthesia, robotic intubation, monitoring cardiac output based on algorithms and ultrasound guidance. In surgery, AI focuses on generating evidence-based, real-time clinical decision support designed to optimize patient care and surgeon workflow. AI can be used to appropriately convey the results of prognosis and treatment algorithms to patients. Nevertheless, there is a lack of problem-solving by AI and a continuing dependence of human analysis.