ReviewWorld journal of gastroenterology2021
Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.
Review in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed, 3 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- Multi-Omics Biomarkers for Predicting Efficacy of Biologic and Small-Molecule Therapies in Adults With Inflammatory Bowel Disease: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.BMC cancer · 2021Pooled it
- From Conventional Therapy to Precision Medicine in Inflammatory Bowel Disease: A State-of-the-Art Review.Biomedicines · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Inflammatory bowel diseases: pathological mechanisms and therapeutic perspectives.Molecular biomedicine · 2026Review
- Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn's disease: a retrospective cross-sectional validation study.Frontiers in immunology · 2026Article
- Review
- Comparative evaluation of a hybrid machine learning-human adjudication paradigm for endoscopic scoring in ulcerative colitis.BMJ open gastroenterology · 2025Article
- Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease.Gut microbes · 2025Article
- Interpretable Artificial Neural Network Models for Predicting Anti-Adalimumab Immune Complex and Serum Drug Level in Crohn's Disease: A Proof-of-Concept Study.Pharmaceutics · 2025Article
- Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges.World journal of gastroenterology · 2025Article
- Prospects of AI-Powered Bowel Sound Analytics for Diagnosis, Characterization, and Treatment Management of Inflammatory Bowel Disease.Medical sciences (Basel, Switzerland) · 2025Review
- Deep learning model for predicting lymph node metastasis around rectal cancer based on rectal tumor core area and mesangial imaging features.BMC medical imaging · 2025Article
- Gut Microbiome Signatures in Multiple Sclerosis: A Case-Control Study with Machine Learning and Global Data Integration.Biomedicines · 2025Article
- Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.BMC microbiology · 2025Article
- Multiomics-Based Profiling of the Fecal Microbiome Reveals Potential Disease-Specific Signatures in Pediatric IBD (PIBD).Biomolecules · 2025Article
- Artificial intelligence-driven strategies for managing renal and urinary complications in inflammatory bowel disease.World journal of nephrology · 2025Article
- Is the Transverse Colon Overlooked? Establishing a Comprehensive Colonoscopy Database from a Multicenter Cluster-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.BMC medical informatics and decision making · 2025Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.