Published online May 7, 2021. doi: 10.3748/wjg.v27.i17.1920
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.
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.