Bhagavathula AS, Al Qady AM, Aldhaleei WA. Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review. World J Gastroenterol 2025; 31(23): 106836 [DOI: 10.3748/wjg.v31.i23.106836]
Corresponding Author of This Article
Akshaya Srikanth Bhagavathula, PhD, Associate Professor, Department of Public Health, College of Health and Human Sciences, North Dakota State University, No. 1455 14th Avenue North, Fargo, ND 58102, United States. akshaya.bhagavathula@ndsu.edu
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Jun 21, 2025; 31(23): 106836 Published online Jun 21, 2025. doi: 10.3748/wjg.v31.i23.106836
Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review
Akshaya Srikanth Bhagavathula, Ahmed Mourtada Al Qady, Wafa A Aldhaleei
Akshaya Srikanth Bhagavathula, Department of Public Health, College of Health and Human Sciences, North Dakota State University, Fargo, ND 58102, United States
Ahmed Mourtada Al Qady, Division of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, FL 32607, United States
Wafa A Aldhaleei, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, United States
Author contributions: Bhagavathula AS made conceptualization and validation; Bhagavathula AS and Aldhaleei WA contributed to methodology, data curation, and review and edit the manuscript; Aldhaleei WA contributed to supervision; Al Qady AM and Aldhaleei WA contributed to writing original draft. All authors contributed to investigation and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Akshaya Srikanth Bhagavathula, PhD, Associate Professor, Department of Public Health, College of Health and Human Sciences, North Dakota State University, No. 1455 14th Avenue North, Fargo, ND 58102, United States. akshaya.bhagavathula@ndsu.edu
Received: March 12, 2025 Revised: April 21, 2025 Accepted: May 30, 2025 Published online: June 21, 2025 Processing time: 100 Days and 16.6 Hours
Abstract
BACKGROUND
Irritable bowel syndrome (IBS) affects approximately 9%-12% of the global population, presenting substantial diagnostic challenges due to symptom subjectivity and lack of definitive biomarkers.
AIM
To systematically examine the diagnostic accuracy of artificial intelligence (AI) models applied to various biomarkers in IBS diagnosis.
METHODS
A comprehensive search of six databases identified 18053 articles published up to May 31, 2024. Following screening and eligibility criteria, six observational studies involving 1366 participants from the United Kingdom, China, and Japan were included. Risk of bias and reporting quality were assessed using quality assessment of diagnostic accuracy studies-2, prediction model risk of bias assessment tool-AI, and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis-AI tools. Key metrics included sensitivity, specificity, accuracy, and area under the curve (AUC).
RESULTS
The included studies applied AI models such as random forests, support vector machines, and neural networks to biomarkers like fecal microbiome composition, gas chromatography data, neuroimaging features, and protease activity. Diagnostic accuracy ranged from 54% to 98% (AUC: 0.61-0.99). Models using fecal microbiome data achieved the highest performance, with one study reporting 98% sensitivity and specificity (AUC = 0.99). While most studies demonstrated high methodological quality, significant variability in datasets, biomarkers, and validation methods limited meta-analysis feasibility and generalizability.
CONCLUSION
AI models show potential to improve IBS diagnostic accuracy by integrating complex biomarkers which will aid the development of algorithms to direct treatment strategies. However, methodological inconsistencies and limited population diversity underscore the need for standardized protocols and external validation to ensure clinical applicability.
Core Tip: This study highlights the transformative potential of artificial intelligence (AI) in irritable bowel syndrome diagnosis by leveraging complex biomarkers such as fecal microbiome composition and neuroimaging features. By systematically evaluating the performance of various AI models, it reveals both their strengths and limitations, with some achieving near-perfect accuracy. However, significant variability in study methodologies and dataset heterogeneity pose challenges to clinical implementation. The findings emphasize the need for standardized validation protocols to enhance reproducibility and real-world applicability. As AI continues to evolve, its integration into irritable bowel syndrome diagnostics could refine precision medicine approaches, offering a data-driven alternative to current symptom-based diagnostic criteria.
