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Articles Published Processes
6/20/2025 9:55:21 AM | Browse: 15 | Download: 56
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Received |
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2025-03-12 04:05 |
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Peer-Review Started |
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2025-03-12 04:06 |
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To Make the First Decision |
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Return for Revision |
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2025-04-10 07:58 |
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Revised |
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2025-04-21 11:51 |
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Second Decision |
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2025-05-30 02:43 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-05-30 05:41 |
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Articles in Press |
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2025-05-30 05:41 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-06-16 02:54 |
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Publish the Manuscript Online |
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2025-06-20 09:55 |
| 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/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Akshaya Srikanth Bhagavathula, Ahmed Mourtada Al Qady and Wafa A Aldhaleei |
| ORCID |
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| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2025-06-20 09:55 |
| Citation |
<p>Bhagavathula AS, Al Qady AM, Aldhaleei WA. Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review. <i>World J Gastroenterol</i> 2025; 31(23): 106836</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i23/106836.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i23.106836 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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