SynthesisNeurogastroenterology and motility2026
Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.
Synthesis in Neurogastroenterology and motility, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Artificial Intelligence in High-resolution Anorectal Manometry: Current Applications and Future Directions.Current gastroenterology reports · 2026Review
- Artificial Intelligence for Diagnosis of Esophageal Manometry: A Narrative Review.Current gastroenterology reports · 2026Review
- Breadth Without Stratification - Why AI Motility Reviews Must Distinguish Feasibility From Clinical Readiness.Neurogastroenterology and motility · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe assessment of gastrointestinal (GI) motility disorders is limited by the invasive, resource-intensive, and subjective nature of current testing. Artificial Intelligence (AI) is being applied to address these challenges. This review aimed to comprehensively identify, appraise, and synthesize the spectrum of AI applications in GI motility diagnostics.
methodsA systematic search of PubMed, Embase, Scopus, Web of Science, and Medline for original studies was conducted in March 2025. Results were narratively synthesized and grouped by anatomic region. KEY
resultsOf 1383 articles, 90 primary studies met the inclusion criteria. In the esophagus (n = 31), the primary focus was automation of High-Resolution Esophageal Manometry (HREM), pH impedance, and Functional Luminal Imaging Probe (FLIP) panometry analysis. In the gastroduodenum (n = 21), studies looked to improve diagnostic accuracy and interpretability of legacy electrogastrography (EGG). In the small and large intestine (n = 19), AI was used to determine transit time and predict post-operative ileus. In the anorectum (n = 9), tools focused on standardizing manometry interpretation. Review of pan-GI applications (n = 10) highlighted AI's application to wireless capsule endoscopy and bowel sounds analysis. While promising, all areas need more rigorous research and external validation before widespread deployment. CONCLUSIONS AND INFERENCES: AI will play an essential role in improving and automating the interpretation of GI motility diagnostics. However, even the most promising models require large-scale prospective validation before clinical implementation.
trial registrationPROSPERO (ID: 1128662).
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.