Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 7, 2021; 27(17): 1920-1935
Published online May 7, 2021. doi: 10.3748/wjg.v27.i17.1920
Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions
John Gubatan, Steven Levitte, Akshar Patel, Tatiana Balabanis, Mike T Wei, Sidhartha R Sinha
John Gubatan, Steven Levitte, Akshar Patel, Tatiana Balabanis, Mike T Wei, Sidhartha R Sinha, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
Author contributions: Gubatan J organized and led the literature review; Levitte S, Balabanis T and Patel A performed the primary literature and data extraction; Gubatan J reviewed literature search results and extracted data for inclusion; Gubatan J drafted the manuscript; Wei MT and Sinha SR provided critical review of the manuscript; all authors interpreted the results and contributed to critical review of the manuscript; Gubatan J had full access to the study data and takes responsibility for the integrity of the data and accuracy of the analysis.
Supported by Chan Zuckerberg Biohub Physician Scientist Scholar Award; and National Institutes of Health NIDDK Loan Repayment Program Award, No. GTQR5718.
Conflict-of-interest statement: The authors have no conflicts of interests or financial disclosures relevant to 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:
Corresponding author: John Gubatan, MD, Academic Research, Consultant Physician-Scientist, Postdoctoral Fellow, Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 420 Broadway Street Pavilion D, 2nd Floor, Redwood City, CA 94063, United States.
Received: January 26, 2021
Peer-review started: January 26, 2021
First decision: February 27, 2021
Revised: March 4, 2021
Accepted: April 13, 2021
Article in press: April 13, 2021
Published online: May 7, 2021

Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.

Keywords: Artificial intelligence, Machine learning, Inflammatory bowel disease, Crohn’s disease, Ulcerative colitis, Clinical outcomes

Core Tip: The application of artificial intelligence (AI) in the field of inflammatory bowel disease (IBD) has grown significantly in the past decade. AI has been used to analyze genomic datasets, construct IBD risk prediction models, and increase IBD diagnosis precision. Machine learning has been used to analyze endoscopic images to improve disease severity grading. AI has enabled the integration of large clinical and laboratory datasets with gene expression profiles to predict clinical outcomes such as therapy response. Future studies will need to validate these findings in independent cohorts and determine whether applying these AI-derived prediction models improves clinical outcomes in IBD.