Publication Name	World Journal of Gastroenterology
Manuscript ID	106836
Country	United States

Category	Gastroenterology & Hepatology
Manuscript Type	Systematic Reviews
Article Title	Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review
Manuscript Source	Invited Manuscript
All Author List	Akshaya Srikanth Bhagavathula, Ahmed Mourtada Al Qady and Wafa A Aldhaleei
Funding Agency and Grant Number
Corresponding Author	Akshaya Srikanth Bhagavathula, Associate Professor, PhD, 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
Key Words	Artificial intelligence; Machine learning; Irritable bowel syndrome; Diagnosis; Systematic review
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.
Citation	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; In press

Received	2025-03-12 04:05
Peer-Review Started	2025-03-12 04:06
To Make the First Decision
Return for Revision	2025-04-10 07:58
Revised	2025-04-21 11:51
Second Decision	2025-05-30 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-30 05:41
Articles in Press	2025-05-30 05:41
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Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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: http://creativecommons.org/Licenses/by-nc/4.0/
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