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Artificial Intelligence in IBD: How Will It Change Patient Management?

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Abstract

Purpose of review

Artificial intelligence (AI) is quickly evolving and will be integral to the management of IBD patients. In this review, we provide an overview of technologies powering applications for Crohn’s disease and ulcerative colitis, with particular attention to information extraction from imaging and text records.

Recent findings

Recent data highlight machine learning capability to replicate expert interpretation and judgment at population scale with accuracy in endoscopic and histology disease grading. Computer vision techniques are also detecting important findings difficult for even expert clinicians to reliably appreciate, including dysplasia on colonoscopy and fibrosis on CT and MRI imaging. Further, segmentation and radiomics can extract more information than manually possible, enabling new possibilities for disease measurement. Finally, natural language processing (NLP) is showing promise automatically extracting IBD-related medical concepts from text and generating conversational responses for both clinicians and patients.

Summary

Quickly mounting evidence supports AI reliability grading disease severity in endoscopy, histology, and imaging. In particular use cases, AI can surpass the ability of humans for disease feature detecting and the generation of new measures of IBD activity. Ultimately, AI-powered information extraction will provide significant incremental improvement in the personalization predictions of outcomes and treatment course in IBD.

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References and Recommended Reading

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Funding

RWS is supported by the National Institute of Health NIDDK (R01DK124779).

Author information

Authors and Affiliations

  1. Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA

    Molly L. Stone MD & Ryan W. Stidham MD, MS, AGAF

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, 3912 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA

    Ryan W. Stidham MD, MS, AGAF

  3. Michigan Institute for Data Science (MIDAS), University of Michigan, 3912 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA

    Ryan W. Stidham MD, MS, AGAF

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Contributions

MS performed literature search and drafted manuscript. RS revised the manuscript.

Corresponding author

Correspondence to Ryan W. Stidham MD, MS, AGAF.

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Conflict of Interest

Molly Stone declares that she has no conflict of interest. Ryan Stidham declares that he has no conflict of interest. RWS has served as a consultant or on advisory boards for AbbVie, Bristol Myers Squibb, Janssen, Takeda, Gilead, Eli Lilly, Exact Sciences, and CorEvitas and holds intellectual property on cross-sectional imaging and endoscopic analysis technologies licensed by the University of Michigan to AMI, llc, EIQ, llc, and PathwaysGI, Inc.

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Stone, M.L., Stidham, R.W. Artificial Intelligence in IBD: How Will It Change Patient Management?. Curr Treat Options Gastro 21, 365–377 (2023). https://doi.org/10.1007/s11938-023-00437-x

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