ReviewDiagnostics (Basel, Switzerland)2023
The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review.World journal of gastroenterology · 2025Pooled it
- The Potential of Complementary and Alternative Therapies in Improving Diarrhea-Predominant Irritable Bowel Syndrome: Strategies for Long-Term Efficacy.Digestive diseases and sciences · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- The reliability and quality of short videos as health information of guidance for bowel sounds: a cross-sectional study.Frontiers in public health · 2025Article
- Microbiome Shifts and Their Impact on Gut Physiology in Irritable Bowel Syndrome.International journal of molecular sciences · 2024Review
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- The Neutrophil-to-Albumin Ratio (NAR): A Novel Index in Relation to Clinical Symptoms, Quality of Life, and Psychological Status in Diarrhea-Predominant Irritable Bowel Syndrome (IBS-D).Journal of inflammation research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual's microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.
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.