The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Currently, there is significant heterogeneity in terms of the datasets AI algorithms are trained with within algorithm types themselves13,14.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ng, S. C., Shi, H. Y., Hamidi, N., Underwood, F. E., Tang, W., Benchimol, E. I., Panaccione, R., Ghosh, S., Wu, J. C. Y., Chan, F. K. L., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32448-0 · Cited by 6,087
Daperno, M., D’Haens, G., Assche, G. V., Baert, F., Bulois, P., Maunoury, V., Sostegni, R., Rocca, R., Pera, A., Gevers, A., et al. (2004) · Gastrointestinal Endoscopy · doi:10.1016/s0016-5107(04)01878-4 · Cited by 1,779
Lewis, J. D., Chuai, S., Nessel, L. A., Lichtenstein, G. R., Aberra, F. N., & Ellenberg, J. H. (2008) · Inflammatory Bowel Diseases · doi:10.1002/ibd.20520 · Cited by 965
Environmental triggers in IBD: a review of progress and evidence
Ananthakrishnan, A. N., Bernstein, C. N., Iliopoulos, D., Macpherson, A., Neurath, M. F., Ali, R. A. R., Vavricka, S. R., & Fiocchi, C. (2018) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.136 · Cited by 948
Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317500 · Cited by 865
Current and emerging therapeutic targets for IBD
Neurath, M. F. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2016.208 · Cited by 717
Artificial intelligence, machine learning and health systems
Panch, T., Szolovits, P., & Atun, R. A. (2018) · Journal of Global Health · doi:10.7189/jogh.08.020303 · Cited by 679
Gillen, C. D., Walmsley, R. S., Prior, P., Andrews, H. A., & Allan, R. N. (1994) · Gut · doi:10.1136/gut.35.11.1590 · Cited by 515
Wang, W., & Siau, K. (2019) · Journal of Database Management · doi:10.4018/jdm.2019010104 · Cited by 452
The IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447
Cited by
Sorted by citations
Items 101–121 of 121
Mousa, K. H., & Nassar, A. E. (2022) · Highlights in BioScience · doi:10.36462/h.biosci.202202
CANCER PREDICTION IN INFLAMMATORY BOWEL DISEASE PATIENTS BY USING MACHINE LEARNING ALGORITHMS
Moayedi, F., Karimi, J., & Dashti, S. E. (2023) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237223500114
Alfonso, G., & Alfonso Pérez, G. (2023) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) · doi:10.6035/14122.2023.851383
Ahammed, S. (2024) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2024.17.2.0074
Guo, Y.-X., Xiong, Y., Liu, X.-C., Liu, Y., & Liu, C. (2025) · World Journal of Nephrology · doi:10.5527/wjn.v14.i1.100825
Practical and Impactful Tips for Private Industry Collaborations With Gastroenterology Practices
Martin, J. A., Appalaneni, V., Gupta, E., & Khaykis, I. B. (2025) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2025.01.021
Xu, L., Hu, X. H., & Bi, Q. (2025) · Research Square · doi:10.21203/rs.3.rs-6921885/v1
Leong, R. W. L., Chaemsupaphan, T., Lin, H., Lim, W. C., Ooi, C. J., Chetwood, J. D., Mao, R., Wu, H., Wei, S. C., Makharia, G. K., et al. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251363703 · Cited by 1
Sun, J., Zhen, J., Liu, C., Jiang, C., Shi, J., Wu, K., & Dong, W. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251359141
“16,17 However, there are currently no studies related to the application of ML algorithms to assess IBD-related sleep disorders.”
Raimundo, A. J., Santos, D. M. D. S., da Silva, E. C., Mendonça, V. F. D. C. R., & Damian, J. O. S. (2025) · Caderno Pedagógico · doi:10.54033/cadpedv22n11-215
Genaro, L. M., Carron, J., Lourenço, G. J., Nagasako, C. K., Reis, G. F. S. R., Camargo, M. G., Oliveira, P. D. S. P., Lima, C. S. P., Ayrizono, M. D. L. S., de Azevedo, A. T., et al. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17121536
“Emerging studies have highlighted the utility of ANNs in supporting tasks such as disease phenotyping, predicting endoscopic activity, and therapeutic response models through the combination of clinical, biochemical, and imaging parameters [14,22–24].”
Bedrikovetski, S., Dudi-Venkata, N. N., Kroon, H. M., Seow, W., Vather, R., Carneiro, G., Moore, J. W., & Sammour, T. (2021) · Figshare · doi:10.6084/m9.figshare.16682841
Artificial intelligence in multi-omics analysis of gastrointestinal diseases
Sarkar, D. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.02.003
The Contribution of Endoscopy in IBD
Badre, W., Rhaoussi, F. Z. E., & Tahiri, M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014215
Pathan, S., Shahini, A., Salvi, M., Ali, T., Lewis, J. R., Molinari, F., & Acharya, U. R. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70074
Zuo, Y. (2026) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519426500387
Machine Learning’s Impact on Advancing Gastrointestinal Diagnosis and Therapeutic Approaches
Garg, A., Pandey, N., Kaur, S., Kulandhaivel, M., Francis, G. A., Bekal, S. K., & Wong, L. S. (2025) · Asian Conference on Communication and Networks (ASIANComNet) · doi:10.1109/asiancomnet68615.2025.11579596
Privacy and Data Protection: Ensuring Compliance with Federated Learning in the Digital Age
Chauhan, G. S., Gollapalli, V. S. T., Srinivasan, K., Jadon, R., & Gutata, G. R. (2025) · Journal of Science and Technology · doi:10.46243/jst.2025.v10.i07.pp29-36
BAYDİLİ, I., Taşçı, B., Doğan, Ş., & Tuncer, T. (2026) · Diagnostics · doi:10.3390/diagnostics16193182
“…activity in inflammatory bowel disease, predict relapse and treatment response, evaluate liver fibrosis and focal liver lesions, estimate prognosis in chronic liver disease and hepatocellular carcinoma, and support the diagnosis and risk stratification of pancreatic and biliary disorders [4,5].”
Khan, I., Khan, I., Noor, U., Irfan, M., & Zhiqiang, L. (2026) · Archives of Microbiology · doi:10.1007/s00203-026-05209-6
“In inflam - matory bowel disease (IBD), machine learning algorithms integrating microbiome and clinical data have demonstrated up to 90% accuracy in distinguishing Crohn’s disease from ulcerative colitis, aiding in differential diagnosis (Gubatan et al. 2021 ).”
Ma, P., Zhou, H. F., Wu, H. B., Li, Y., & Yuan, G. (2026) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-026-03694-x
Metadata
- Authors
- Gubatan, J., Levitte, S., Patel, A., Balabanis, T., Wei, M. T., & Sinha, S. R.
- Affiliations
- Stanford Medicine (US)
- 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)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
