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Articles Published Processes
6/15/2026 8:38:28 AM | Browse: 1 | Download: 1
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Received |
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2025-12-16 01:35 |
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Peer-Review Started |
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2025-12-16 01:35 |
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First Decision by Editorial Office Director |
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2026-01-16 06:56 |
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Return for Revision |
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2026-01-16 06:56 |
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Revised |
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2026-01-18 22:11 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-02-09 02:42 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-09 08:16 |
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Articles in Press |
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2026-02-09 08:16 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-07 10:47 |
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Publish the Manuscript Online |
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2026-06-15 08:38 |
| ISSN |
2220-6124 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
The Author(s) 2026. 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 |
Urology & Nephrology |
| Manuscript Type |
Review |
| Article Title |
Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Kirolos Eskandar |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Kirolos Eskandar, MD, Researcher, Medicine and Surgery, Helwan University, Al Masaken Al Iqtisadeyah, Helwan, Cairo Governorate 4034572, Giza 11795, Al Jīzah, Egypt. kirolos210575@med.helwan.edu.eg |
| Key Words |
Machine learning; Deep learning; Precision nephrology; Clinical decision support; Multimodal data integration |
| Core Tip |
Artificial intelligence (AI) holds significant promise for transforming chronic kidney disease (CKD) care by integrating longitudinal, high-dimensional, and multimodal clinical data to support earlier detection, improved risk stratification, and personalized management. This review synthesizes recent evidence on AI applications across the CKD continuum, including early detection, risk prediction, personalized treatment, complication management, dialysis, and kidney transplantation. Beyond reporting model performance, it critically examines validation quality, calibration, explainability, equity, and real-world implementation barriers, highlighting the persistent gap between methodological innovation and routine clinical deployment. The review outlines key priorities required to translate AI tools into safe, equitable, and clinically meaningful CKD care. |
| Publish Date |
2026-06-15 08:38 |
| Citation |
Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719
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| URL |
https://www.wjgnet.com/2220-6124/full/v15/i2/117719.htm |
| DOI |
https://doi.org/10.5527/wjn.v15.i2.117719 |
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