Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 7, 2021; 27(37): 6191-6223
Published online Oct 7, 2021. doi: 10.3748/wjg.v27.i37.6191
Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology
Chrysanthos D Christou, Georgios Tsoulfas
Chrysanthos D Christou, Georgios Tsoulfas, Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
Author contributions: Christou CD performed the screening of articles for eligibility and drafted the manuscript; Tsoulfas G performed the screening of articles for eligibility and edited the manuscript.
Conflict-of-interest statement: The authors declare no conflict of interest for this article.
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:
Corresponding author: Georgios Tsoulfas, FACS, FICS, MD, Associate Professor, Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, 66 Tsimiski Street, Thessaloniki 54622, Greece.
Received: January 31, 2021
Peer-review started: January 31, 2021
First decision: March 14, 2021
Revised: May 6, 2021
Accepted: August 31, 2021
Article in press: August 31, 2021
Published online: October 7, 2021

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Keywords: Artificial intelligence, Machine learning, Gastroenterology, Hepatology, Artificial neural networks, Support vector machine

Core Tip: The opportunities that arise from the application of artificial intelligence/machine learning-based models in gastroenterology and hepatology include the establishment of targeted screening programs through the identification of patients prone to develop cancer, the development of non-invasive diagnostic tools, the improvement of the diagnostic accuracy, the development of treatment allocation frameworks based on predictions of outcomes for different treatment modalities, the development of models to ensure cost-effective use of resources, the development of triage tools for higher levels of care and decision-making tools for further treatment, based on individualized patient outcome predictions, and finally the development of predictive models of prognosis for patient and family counseling.