Cited by 121Inflammatory Bowel DiseaseArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

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Gubatan, John, Levitte, Steven, Patel, Akshar, Balabanis, Tatiana, Wei, Mike T, Sinha, Sidhartha R·2021·World Journal of Gastroenterology

DOI 10.3748/wjg.v27.i17.1920PMC PMC8108036

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Abstract

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.

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Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection
Hassan, A., Abujabal, H. A. S., Ahmad, S., Malebary, S. J., & Alromema, N. A. (2024) · Diagnostics · doi:10.3390/diagnostics14111182 · Cited by 6
Gastrointestinal disorders in children with autism: Could artificial intelligence help?
Al‐Beltagi, M., Saeed, N. K., & Qaraghuli, S. (2022) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v3.i1.1 · Cited by 6
Gut Microbiome Signatures in Multiple Sclerosis: A Case-Control Study with Machine Learning and Global Data Integration
Neklesova, M. V., Sogomonyan, K. S., Golovkin, I. A., Shirokiy, N. I., Vershinina, S. O., Tsvetikova, S. A., Korzhova, J. E., Zakharova, M. N., & Gnedovskaya, E. V. (2025) · Biomedicines · doi:10.3390/biomedicines13081806 · Cited by 6
“…significance of the microbiota in MS. Advances in machine learning (ML) have enabled the identification of disease-specific microbial signatures in conditions such as type 2 diabetes (T2D) [16], inflammatory bowel diseases (IBD) [17], colorectal cancer (CRC) [18], and neurological disorders [19].”
Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning
Jeong, J., Hong, S. T., Ullah, I., Kim, E. S., & Park, S. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020288 · Cited by 5
“To the best of our knowledge, no study has classiﬁed CRC and IBD by using a machine learning-based method.”
MethodInfluential
Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
Gavrilescu, O., Popa, I. V., Dranga, M., Mihai, R., Prelipcean, C. C., & Mihai, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12113609 · Cited by 5
Digital Twins in Architecture
Horvath, A.-S., & Pouliou, P. (2024) · CRC Press eBooks · doi:10.1201/9781003425724-46 · Cited by 7
Is the Transverse Colon Overlooked? Establishing a Comprehensive Colonoscopy Database from a Multicenter Cluster-Randomized Controlled Trial
Cold, K. M., Vamadevan, A., Heen, A., Vilmann, A. S., Rasmussen, M., Konge, L., & Svendsen, M. B. S. (2025) · Diagnostics · doi:10.3390/diagnostics15050591 · Cited by 5
Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease
Ricci, L., Toussaint, Y., Becker, J., Najjar, H., Renier, A., Choukour, M., Buisson, A., Devos, C., Epstein, J., Peyrin‐Biroulet, L., et al. (2021) · Digestive and Liver Disease · doi:10.1016/j.dld.2021.09.005 · Cited by 7
Thick Data Analytics for Rating Ulcerative Colitis Severity Using Small Endoscopy Image Sample
Fiaidhi, J., Zezos, P., & Mohammed, S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671327 · Cited by 5
“…learning has helped the analysis of large datasets in UC/CD conditions and in clustering the severity of these conditions reaching the level of human reviewer [3], significant heterogeneity exists in machine learning methods relevant to the available datasets with varying clinical outcomes [4].”
Background
Artificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease
Marques, K. F., Marques, A. F., Lopes, M. A., Beraldo, R. F., Lima, T. B., & Sassaki, L. Y. (2022) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v3.i1.1 · Cited by 5
Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges
Liu, Z.-G., & Xie, S. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i42.112107 · Cited by 6
“Studies suggest that radiomics can provide objective measures of mucosal inflammation, potentially complementing or substituting for endoscopy in certain scenarios[23,24].”
CellMixer: Pathological image classification using dual-branch VMamba with randomly mixing gradient features data augmentation
Chai, E., Gu, X., Lu, Z., & Cui, T. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131298 · Cited by 4
The global research of artificial intelligence on inflammatory bowel disease: A bibliometric analysis
Zeng, S., Dong, C., Liu, C., Zhen, J., Pu, Y., Hu, J., & Dong, W. (2025) · Digital Health · doi:10.1177/20552076251326217 · Cited by 4
Glycoproteomics-Based Liquid Biopsy: Translational Outlook for Colorectal Cancer Clinical Management in Southeast Asia
Kumarasamy, G., Salim, N. H. M., Afandi, N. S. M., Habib, M. A. H., Amin, N. D. M., Ismail, M. N., & Musa, M. (2023) · Future Oncology · doi:10.2217/fon-2023-0704 · Cited by 4
Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis
Chen, Y. F., Liu, L., Lü, B., Yang, Y., Zheng, S. S., Huang, X., Xu, Y., & Fan, Y. H. (2024) · Journal of Digestive Diseases · doi:10.1111/1751-2980.13308 · Cited by 4
“15 In addition to its utility in endoscopic techniques, AI has also been reported to have a high accuracy in evaluating the severity of IBD based on clinical data of the patients, such as biochemical parameters, with algorithms such as CNN and SVM. 16 Studies have employed ML algorithms to predict disease risk based o…”
Influential
Deep learning model for predicting lymph node metastasis around rectal cancer based on rectal tumor core area and mesangial imaging features
Guo, L., Fu, K., Wang, W., Zhou, L. Q., Chen, L., & Jiang, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01878-9 · Cited by 3
“AI has been employed for assessing colonic polyps [24], adenomas [25], colorectal cancer [26], ulcerative colitis [27], and intestinal motility disorders in colorectal diseases [28].”
Machine learning approach and internet of things technologies to unravel the complex interaction between microbiome-metabolome in inflammatory bowel disease: a new frontier in precision medicine
Palmieri, O., Cannarozzi, A. L., Latiano, A., Massimino, L., Bossa, F., Riva, M., Ungaro, F., Guerra, M., Brina, A. L. P. D., Carparelli, S., et al. (2025) · Gut Pathogens · doi:10.1186/s13099-025-00758-5 · Cited by 3
“Among the applications of AI in IBD, the integration and analysis of multi-omics data have enabled a better understanding of disease pathogenesis and the identification of diagnostic and therapeutic biomarkers [30].”
Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges
Suarez, M., Martínez, R., González-Martínez, F., Torres, A. M., & Mateo, J. (2026) · World Journal of Hepatology · doi:10.4254/wjh.v18.i2.114834 · Cited by 4
Artificial intelligence and the evolution of pathology: Scaling from digital diagnostics to multimodal precision medicine
Huynh, C. D. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100386 · Cited by 4
Acute colonic flexures: the basis for developing an artificial intelligence-based tool for predicting the course of colonoscopy
Woźniak, S., Pawluś, A., Grzelak, J., Chobotow, S., Paulsen, F. P., Olchowy, C., & Zaleska–Dorobisz, U. (2022) · Anatomical Science International · doi:10.1007/s12565-022-00681-8 · Cited by 3
“It can also be used to assess the quality of bowel preparation before the examination (Gubatan et al. 2021; Yang and Bang 2019; Yoo et al. 2021).”
Method
Comparison of the effect of self-care education with two methods, teach-back and smartphone application, on the adherence to treatment in patients with inflammatory bowel disease
Manzari, Z. S., Ghaderi, M. S., Vossoughinia, H., Rafiei, H., & Mafi, M. H. (2024) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_200_24 · Cited by 3
Central resources of variant discovery and annotation and its role in precision medicine
Halim‐Fikri, H., Syed-Hassan, S.-N. R.-K., Juhari, W. K. W., Assyuhada, M. G. S. N., Hernaningsih, Y., Yusoff, N. M., Merican, A. F., & Zilfalil, B. A. (2022) · Asian Biomedicine · doi:10.2478/abm-2022-0032 · Cited by 3
“To illustrate, Gubatan et al. [81] explained the most recent advanced technologies of AI in disease diagnosis, risk prediction, and disease severity evaluation, as well as prediction of clinical decision-making in IBD patients.”
Background
Predictive approaches in inflammatory bowel disease
Pinton, P. (2021) · Clinical and Translational Science · doi:10.1111/cts.13179 · Cited by 3
Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort
Marzullo, A., Savevski, V., Menini, M., Schilirò, A., Franchellucci, G., Dal Buono, A., Bezzio, C., Gabbiadini, R., Hassan, C., Repici, A., et al. (2025) · Data · doi:10.3390/data10070100 · Cited by 2
Biomarkers for Disease Activity in Inflammatory Bowel Disease
Devi, J., Samaan, S., Syal, G., & Deepak, P. (2025) · Gastroenterology Clinics of North America · doi:10.1016/j.gtc.2025.08.003 · Cited by 2
Next-generation non-animal models for inflammatory bowel disease: In vitro and in silico approaches for mechanistic understanding
Chougule, P. R., & Sinha, S. N. (2025) · NAM journal. · doi:10.1016/j.namjnl.2025.100051 · Cited by 2
“The application of AI and ML in IBD has expanded rapidly, providing powerful in silico tools to analyse and interpret complex datasets, ranging from genetic and molecular signatures to histological images, endoscopic data, and electronic health records (Gubatan et al., 2021).”
Self-citation
IOlBD evidence-based consensus on the use of artificial intelligence for assessment of endoscopic and histologic endpoints in clinical trials of inflammatory bowel disease
Dignass, A. U., Narula, N., Pratap, K., Danese, S., Dotan, I., Iacucci, M., Jairath, V., Loftus, E. V., Magro, F., Mantzaris, G. J., et al. (2026) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjag007 · Cited by 2
Machine Learning for the Analysis of Human Microbiome in Inflammatory Bowel Diseases: Literature Review
najih, N. E., & Moussa, P. A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-47366-1_1 · Cited by 1
Artificial intelligence and colonoscopy − enhancements and improvements
Yoo, B. S., D’Souza, S. M., Houston, K. V., Patel, A., Lau, J., Elmahdi, A., Parekh, P. J., & Johnson, D. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i4.157 · Cited by 2
Self-citation
Multiomics-Based Profiling of the Fecal Microbiome Reveals Potential Disease-Specific Signatures in Pediatric IBD (PIBD)
DeSantis, A., Buss, K., Coker, K., Pasternak, B. A., Chi, J., Patterson, J. S., Gu, H., Jurutka, P. W., & Sandrin, T. R. (2025) · Biomolecules · doi:10.3390/biom15050746 · Cited by 1
“Leveraging machine learning and AI may also enhance predictive modeling for PIBD diagnosis and biomarker discovery [84].”
Comparative evaluation of a hybrid machine learning-human adjudication paradigm for endoscopic scoring in ulcerative colitis
Gottlieb, K. T., Battioui, C., Wang, Y., Brodskiy, P., Haft‐Javaherian, M., Colucci, D. R., Thomason, D., & Baxi, S. S. (2025) · BMJ Open Gastroenterology · doi:10.1136/bmjgast-2025-001959 · Cited by 1
“6 7 16–18 While these endeavours have proven worthy, with several AI models exhibiting high agreement with human readers, relying solely on these models for central reading poses its own set of limitations, including a lack of regulatory guidance, unclear acceptance criteria and insufficient…”
Priorities for advancing artificial intelligence in inflammatory bowel disease: a technology update
Silverman, A. L., Stidham, R. W., Parasa, S., Sharma, P., & Mahadevan, U. (2026) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2025.12.248 · Cited by 1
From Conventional Therapy to Precision Medicine in Inflammatory Bowel Disease: A State-of-the-Art Review
Almajdi, A., & Shehab, M. (2026) · Biomedicines · doi:10.3390/biomedicines14040798 · Cited by 1
“Collectively, AI serves as a critical tool for personalized IBD care, supporting treat-ment decisions that reflect each patient’s unique biological, clinical, and molecular profile [38].”
Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn’s disease: a retrospective cross-sectional validation study
Jiang, X., Li, P., Chen, Z., Yao, H., Jia, H., Wang, T., & Tang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1841261 · Cited by 1
Artificial intelligence in gastrointestinal endoscopy: evolution to a new era
Ortíz, O., Esparrach, M. G. F., Daca-Álvarez, M., & Pellisé, M. (2022) · Revista Española de Enfermedades Digestivas · doi:10.17235/reed.2022.8961/2022 · Cited by 1
Artificial intelligence: machine learning, deep learning, and applications in gastrointestinal endoscopy
Afonso, J. P. L., Martins, M., Ferreira, J. P. S., & Mascarenhas, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99647-1.00003-4 · Cited by 2
Forecast trend of artificial intelligence and machine learning technology: prospects and implications in the field of gastrointestinal endoscopy
Karakasidis, E. (2025) · Health and Technology · doi:10.1007/s12553-025-00997-1 · Cited by 1
Utility of artificial intelligence in colonoscopy
Shah, N., Jyala, A., Patel, H., & Makker, J. S. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i3.79
Utility of artificial intelligence in colonoscopy
Shah, N., Jyala, A., Patel, H., & Makker, J. S. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i3.78
Deep Learning Framework for Cancer Diagnosis and Treatment
Bahadur, S., & Kumar, P. (2022) · doi:10.1002/9781119857983.ch8

Authors: Gubatan, J., Levitte, S., Patel, A., Balabanis, T., Wei, M. T., & Sinha, S. R.
Affiliations: Stanford Medicine (US)
ORCID: Gubatan, John
Levitte, Steven
Patel, Akshar
Wei, Mike T
Sinha, Sidhartha R
Venue: World Journal of Gastroenterology
Publisher: Baishideng Publishing Group Inc.
Published: 2021-05-07
Volume: 27
Issue: 17
Pages: 1920-1935
ISSN: 1007-9327 (ISSN-L)
DOI: 10.3748/wjg.v27.i17.1920
PMC: PMC8108036
OpenAlex: W3157079437
Cited by: 121
References: 94
Citations by year: 2026: 15 · 2025: 28 · 2024: 21 · 2023: 21 · 2022: 20 · 2021: 16
FWCI: 2.87
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: World Journal of Gastroenterology (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Gastroenterology › Inflammatory Bowel Disease
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: inflammatory bowel disease, artificial intelligence, machine learning, diagnosis, risk prediction, disease severity assessment, clinical outcome prediction, multi-omics data, endoscopy, endomicroscopy, electronic health records, clinical validation
MeSH terms: Artificial Intelligence, Colitis, Humans, Prospective Studies, Inflammatory Bowel Diseases, Metagenomics

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Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

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