Kohli A, Holzwanger EA, Levy AN. Emerging use of artificial intelligence in inflammatory bowel disease. World J Gastroenterol 2020; 26(44): 6923-6928 [PMID: 33311940 DOI: 10.3748/wjg.v26.i44.6923]
Corresponding Author of This Article
Alexander N Levy, MD, Assistant Professor, Attending Doctor, Division of Gastroenterology and Hepatology, Tufts Medical Center, 800 Washington Street, Box 233, Boston, MA 02111, United States. alevy@tuftsmedicalcenter.org
Research Domain of This Article
Gastroenterology & Hepatology
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 Gastroenterol. Nov 28, 2020; 26(44): 6923-6928 Published online Nov 28, 2020. doi: 10.3748/wjg.v26.i44.6923
Emerging use of artificial intelligence in inflammatory bowel disease
Arushi Kohli, Erik A Holzwanger, Alexander N Levy
Arushi Kohli, Department of Internal Medicine, Tufts Medical Center, Boston, MA 02111, United States
Erik A Holzwanger, Alexander N Levy, Division of Gastroenterology and Hepatology, Tufts Medical Center, Boston, MA 02111, United States
Author contributions: Kohli A performed the majority of the writing, prepared the figures and tables; Holzwanger EA performed editing and proofreading; Levy AN provided the input in writing the paper and designed the outline.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript.
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: Alexander N Levy, MD, Assistant Professor, Attending Doctor, Division of Gastroenterology and Hepatology, Tufts Medical Center, 800 Washington Street, Box 233, Boston, MA 02111, United States. alevy@tuftsmedicalcenter.org
Received: August 31, 2020 Peer-review started: August 31, 2020 First decision: September 24, 2020 Revised: October 24, 2020 Accepted: November 13, 2020 Article in press: November 13, 2020 Published online: November 28, 2020 Processing time: 87 Days and 16.3 Hours
Abstract
Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.
Core Tip: Artificial intelligence (AI) is a novel technological advancement that is rapidly growing in the field of inflammatory bowel disease. The use of AI and machine learning has been shown to aid in diagnosing and understanding severity of disease, predicting treatment response along with likelihood of disease recurrence and assisting with colorectal neoplasia screening in this patient population. Further studies are needed to understand the full impact AI may have on improving Crohn’s disease and ulcerative colitis patient outcomes.
