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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.
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“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]
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Sorted by citations
Bias in AI-based models for medical applications: challenges and mitigation strategies
Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00858-z · Cited by 372
Bedrikovetski, S., Dudi-Venkata, N. N., Kroon, H. M., Seow, W., Vather, R., Carneiro, G., Moore, J. W., & Sammour, T. (2021) · BMC Cancer · doi:10.1186/s12885-021-08773-w · Cited by 164
Canine chronic enteropathy—Current state-of-the-art and emerging concepts
Jergens, A. E., & Heilmann, R. M. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.923013 · Cited by 116
Cross-scale multi-instance learning for pathological image diagnosis
Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103124 · Cited by 95
Artificial Intelligence in Genetics
Vilhekar, R. S., & Rawekar, A. (2024) · Cureus · doi:10.7759/cureus.52035 · Cited by 104
“…has made it simpler to analyze, combine, and interpret huge genetics datasets, the requirement for objective prospective validation studies, as well as the substantial heterogeneity in AI methods, datasets, and clinical outcomes, are currently preventing the use of AI in clinical practice [34].”
Background
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 ›
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