ReviewWorld journal of gastroenterology2020
Emerging use of artificial intelligence in inflammatory bowel disease.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.Journal of Crohn's & colitis · 2022Pooled it
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Is the Transverse Colon Overlooked? Establishing a Comprehensive Colonoscopy Database from a Multicenter Cluster-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2025Article
- Use of Modified YOLOv5 Algorithm in the Differential Diagnosis of Colonic Crohn's Disease and Ulcerative Colitis on CTE Images.Gastroenterology research and practice · 2025Article
- Designing the Essential Informational Needs of a Smartphone Application for Self-Management of Patients With Inflammatory Bowel Disease.Health science reports · 2024Article
- Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach.Medicina (Kaunas, Lithuania) · 2023Article
- The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now.Diagnostics (Basel, Switzerland) · 2023Review
- Gut microbiota analysis for prediction of clinical relapse in Crohn's disease.Scientific reports · 2022Observational
- Review
- Artificial intelligence applications in gastroenterology: steps ahead.Medicine and pharmacy reports · 2021Article
- Deep learningWorld journal of gastroenterology · 2021Article
- Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review.Genes · 2021Article
- Review
- Review
- Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population.Frontiers in microbiology · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.