Artificial Intelligence in Inflammatory Bowel Disease (IBD) Diagnostics: Applications and Future Directions

Authors

  • Amine Zoughlami, MD, MA Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada
  • Adel Arezki, MD Division of Urology, Department of Surgery, McGill University Health Centre, Montreal, Quebec, Canada
  • Edgard Medawar, MD Department of Medicine, University of Ottawa Division of Gastroenterology, Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec
  • Talat Bessissow, MD, MDCM, MSc, FRCPC Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada

DOI:

https://doi.org/10.58931/cibdt.2025.3350

Abstract

Key Takeaways

• AI can improve the accuracy, objectivity, and reproducibility of IBD disease assessments across multiple disease assessment indices.

• Multiple AI models have shown expert-level performance in the assessment of endoscopic and histologic activity in IBD.

• The deployment of AI models can help uniformize the quality of disease assessment across academic and community centres alike.

• The next steps will involve multimodal AI models. The development of these models, and the fine-tuning of unimodal systems, will require large, diverse datasets and careful governance. 

Author Biographies

Amine Zoughlami, MD, MA, Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada

Dr. Amine Zoughlami is a fifth-year Adult Gastroenterology resident at McGill University. He completed his medical training and Internal Medicine training at McGill University, and a Master’s in Bioethics at the Universite de Montreal. He will be completing an Advanced Inflammatory Bowel Disease Fellowship at Western University and has an interest in medical innovation and leveraging technological initiatives in gastroenterology.

Adel Arezki, MD, Division of Urology, Department of Surgery, McGill University Health Centre, Montreal, Quebec, Canada

Dr. Adel Arezki is a fourth-year urology resident at McGill University. He completed his medical training at McGill University, with a research focus at the intersection of urology and technology. His work centres on artificial intelligence, clinical prediction modelling and data-driven approaches to improve diagnostic accuracy and treatment outcomes in medicine.

Edgard Medawar, MD, Department of Medicine, University of Ottawa Division of Gastroenterology, Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Quebec

Dr. Edgard Medawar completed his medical training at McGill University. He is currently a third-year resident in internal medicine at the University of Ottawa, and is completing a Ph.D. in Experimental Medicine at the University of Montreal. Next year, he will begin his adult gastroenterology subspecialty training at the University of Montreal. Edgard has an interest in inflammatory bowel diseases and endoscopic tissue resection. 

Talat Bessissow, MD, MDCM, MSc, FRCPC, Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada

Dr. Talat Bessissow earned his medical degree at McGill University (2005) where he then completed post-graduate training in Internal Medicine and Gastroenterology (2005-2010). In 2012, he trained in inflammatory bowel disease and advanced endoscopic imaging at the Gasthuisberg University Hospital, Leuven, Belgium under the supervision of professor Severine Vermeire. He also competed a Master in Experimental Medicine and Epidemiology from McGill University in 2016. Since 2012, He is a full time Associate Professor in the Division of Gastroenterology and Attending Staff at the McGill University Health Center. He is member of the McGill Inflammatory Bowel Disease (IBD) group as well as the McGill small bowel program. His current research focuses on the role and outcomes of mucosal healing in IBD as well as early detection of neoplastic lesions in ulcerative colitis. His researched has led him to publish over 150 peer-reviewed full papers. He is the past president of the Canadian IBD Research Consortium. He has also served as a reviewer for multiple national and international journals. 

References

Turner D, Ricciuto A, Lewis A, D’Amico F, Dhaliwal J, Griffiths AM, et al. STRIDE-II: an update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology. 2021;160(5):1570–1583. doi:10.1053/j.gastro.2020.12.031 DOI: https://doi.org/10.1053/j.gastro.2020.12.031

Sandborn WJ, Feagan BG, Hanauer SB, Lochs H, Lofberg R, Modigliani R, et al. A review of activity indices and efficacy endpoints for clinical trials of medical therapy in adults with Crohn’s disease. Gastroenterology. 2002;122(2):512–530. doi:10.1053/gast.2002.31072 DOI: https://doi.org/10.1053/gast.2002.31072

Burdine LK, Rakowsky S, Grossberg L, Rabinowitz L, Center BIR, Cheifetz AS, et al. Irritable bowel syndrome/inflammatory bowel disease overlap: less common than we think. Gastro Hep Adv. 2024;3(8):1135–1137. Published 2024 Aug 10. doi:10.1016/j.gastha.2024.08.005 DOI: https://doi.org/10.1016/j.gastha.2024.08.005

Fairbrass KM, Costantino SJ, Gracie DJ, Ford AC. Prevalence of irritable bowel syndrome-type symptoms in patients with inflammatory bowel disease in remission: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2020;5(12):1053–1062. doi:10.1016/S2468-1253(20)30300-9 DOI: https://doi.org/10.1016/S2468-1253(20)30300-9

Ricci L, Toussaint Y, Becker J, Najjar H, Renier A, Choukour M, et al. Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease. Dig Liver Dis. 2022;54(4):483–489. doi:10.1016/j.dld.2021.09.005 DOI: https://doi.org/10.1016/j.dld.2021.09.005

Patel PV, Davis C, Ralbovsky A, Tinoco D, Williams CYK, Slatter S, et al. Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in inflammatory bowel disease. Gastro Hep Adv. 2025;4(2):100563. Published 2024 Oct 10. doi:10.1016/j.gastha.2024.10.003 DOI: https://doi.org/10.1016/j.gastha.2024.10.003

Stidham RW, Yu D, Zhao X, Bishu S, Rice M, Bourque C, et al. Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes. Inflamm Bowel Dis. 2023;29(4):503–510. doi:10.1093/ibd/izac109 DOI: https://doi.org/10.1093/ibd/izac109

Hirten RP, Danieletto M, Sanchez-Mayor M, Whang JK, Lee KW, Landell K, et al. Physiological data collected from wearable devices identify and predict inflammatory bowel disease flares. Gastroenterology. 2025;168(5):939–951.e5. doi:10.1053/j.gastro.2024.12.024 DOI: https://doi.org/10.1053/j.gastro.2024.12.024

Shahub S, Kumar RM, Lin KC, Banga I, Choi NK, Garcia NM, et al. Continuous monitoring of CRP, IL-6, and calprotectin in inflammatory bowel disease using a perspiration-based wearable device. Inflamm Bowel Dis. 2025;31(3):647–654. doi:10.1093/ibd/izae054 DOI: https://doi.org/10.1093/ibd/izae054

Khanna R, Zou G, D’Haens G, Rutgeerts P, McDonald JW, Daperno M, et al. Reliability among central readers in the evaluation of endoscopic findings from patients with Crohn’s disease. Gut. 2016;65(7):1119–1125. doi:10.1136/gutjnl-2014-308973 DOI: https://doi.org/10.1136/gutjnl-2014-308973

Gottlieb K, Requa J, Karnes W, Chandra Gudivada R, Shen J, Rael E, et al. Central reading of ulcerative colitis clinical trial videos using neural networks. Gastroenterology. 2021;160(3):710–719.e2. doi:10.1053/j.gastro.2020.10.024 DOI: https://doi.org/10.1053/j.gastro.2020.10.024

Fan Y, Mu R, Xu H, Xie C, Zhang Y, Liu L, et al. Novel deep learning-based computer-aided diagnosis system for predicting inflammatory activity in ulcerative colitis. Gastrointest Endosc. 2023;97(2):335–346. doi:10.1016/j.gie.2022.08.015 DOI: https://doi.org/10.1016/j.gie.2022.08.015

Kim JE, Choi YH, Lee YC, Seong G, Song JH, Kim TJ, et al. Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis. Sci Rep. 2023;13(1):11351. Published 2023 Jul 13. doi:10.1038/s41598-023-38206-6 DOI: https://doi.org/10.1038/s41598-023-38206-6

Khanna R, Feagan BG, Zou G, Stitt LW, McDonald JWD, Bressler B, et al. Reliability and responsiveness of clinical and endoscopic outcome measures in Crohn’s disease. Inflamm Bowel Dis. 2025;31(3):706–715. doi:10.1093/ibd/izae089 DOI: https://doi.org/10.1093/ibd/izae089

Cai L, Wittrup E, Minoccheri C, Hiatt TK, Rice MD, Bishu S, et al. Artificial intelligence for quantifying endoscopic mucosal ulceration in Crohn’s disease. Clin Gastroenterol Hepatol. Published online August 18, 2025. doi:10.1016/j.cgh.2025.05.026 DOI: https://doi.org/10.1016/j.cgh.2025.05.026

Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, Gonzalez-Suarez B, et al. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health. 2024;6(5):e345–e353. doi:10.1016/S2589-7500(24)00048-7 DOI: https://doi.org/10.1016/S2589-7500(24)00048-7

Fan S, Xu L, Fan Y, Wei K, Li L. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol. 2018;63(16):165001. Published 2018 Aug 10. doi:10.1088/1361-6560/aad51c DOI: https://doi.org/10.1088/1361-6560/aad51c

Barash Y, Azaria L, Soffer S, Margalit Yehuda R, Shlomi O, Ben-Horin S, et al. Ulcer severity grading in video capsule images of patients with Crohn’s disease: an ordinal neural network solution. Gastrointest Endosc. 2021;93(1):187–192. doi:10.1016/j.gie.2020.05.066 DOI: https://doi.org/10.1016/j.gie.2020.05.066

Cardoso P, Mascarenhas M, Afonso J, Ribeiro T, Mendes F, Martins M, et al. Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network. Therap Adv Gastroenterol. 2024;17:17562848241251569. Published 2024 May 27. doi:10.1177/17562848241251569 DOI: https://doi.org/10.1177/17562848241251569

Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis. 2024;56(7):1164–1172. doi:10.1016/j.dld.2023.11.005 DOI: https://doi.org/10.1016/j.dld.2023.11.005

Najdawi F, Sucipto K, Mistry P, Hennek S, Jayson CKB, Lin M, et al. Artificial intelligence enables quantitative assessment of ulcerative colitis histology. Mod Pathol. 2023;36(6):100124. doi:10.1016/j.modpat.2023.100124 DOI: https://doi.org/10.1016/j.modpat.2023.100124

Iacucci M, Parigi TL, Del Amor R, Meseguer P, Mandelli G, Bozzola A, et al. Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis. Gastroenterology. 2023;164(7):1180–1188.e2. doi:10.1053/j.gastro.2023.02.031 DOI: https://doi.org/10.1053/j.gastro.2023.02.031

Chen H, Lin X, Pan X, Xu H, Zhang X, Liang G, et al. Development and validation of a blood routine-based extent and severity clinical decision support tool for ulcerative colitis. Sci Rep. 2023;13(1):21368. Published 2023 Dec 4. doi:10.1038/s41598-023-48569-5 DOI: https://doi.org/10.1038/s41598-023-48569-5

Jairath V, Jeyarajah J, Zou G, Parker CE, Olson A, Khanna R, et al. A composite disease activity index for early drug development in ulcerative colitis: development and validation of the UC-100 score. Lancet Gastroenterol Hepatol. 2019;4(1):63–70. doi:10.1016/S2468-1253(18)30306-6 DOI: https://doi.org/10.1016/S2468-1253(18)30306-6

He M, Li C, Tang W, Kang Y, Zuo Y, Wang Y. Machine learning gene expression predicting model for ustekinumab response in patients with Crohn’s disease. Immun Inflamm Dis. 2021;9(4):1529–1540. doi:10.1002/iid3.506 DOI: https://doi.org/10.1002/iid3.506

Stidham RW, Liu Y, Enchakalody B, Van T, Krishnamurthy V, Su GL, et al. the use of readily available longitudinal data to predict the likelihood of surgery in Crohn disease. Inflamm Bowel Dis. 2021;27(8):1328–1334. doi:10.1093/ibd/izab035 DOI: https://doi.org/10.1093/ibd/izab035

Iacucci M, Santacroce G, Meseguer P, Dieguez A, Del Amor R, Kolawole BB, et al. Endo-Histo foundational fusion model: a novel artificial intelligence for assessing histologic remission and response to therapy in ulcerative colitis clinical trial. J Crohns Colitis. 2025;19(7):jjaf108. doi:10.1093/ecco-jcc/jjaf108 DOI: https://doi.org/10.1093/ecco-jcc/jjaf108

Jong MR, Boers TGW, Fockens KN, Jukema JB, Kusters CHJ, Jaspers TJM, et al. GastroNet-5M: a multicenter dataset for developing foundation models in gastrointestinal endoscopy. Gastroenterology. Published online July 30, 2025. doi:10.1053/j.gastro.2025.07.030 DOI: https://doi.org/10.1016/S0016-5085(25)01191-6

Budzyn K, Romanczyk M, Kitala D, Kolodziej P, Bugajski M, Adami HO, et al. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. Lancet Gastroenterol Hepatol. 2025;10(10):896–903. doi:10.1016/S2468-1253(25)00133-5 DOI: https://doi.org/10.1016/S2468-1253(25)00133-5

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Published

2025-12-22

How to Cite

1.
Zoughlami A, Arezki A, Medawar E, Bessissow T. Artificial Intelligence in Inflammatory Bowel Disease (IBD) Diagnostics: Applications and Future Directions. Can IBD Today [Internet]. 2025 Dec. 22 [cited 2025 Dec. 26];3(3):22–27. Available from: https://canadianibdtoday.com/article/view/3-3-Zoughlami_et_al

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Volume 3, Issue 3, Fall 2025

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