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Articles Published Processes
9/2/2026 6:21:35 AM | Browse: 0 | Download: 0
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Received |
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2026-04-20 08:30 |
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Peer-Review Started |
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2026-04-20 08:31 |
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First Decision by Editorial Office Director |
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2026-05-28 11:59 |
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Return for Revision |
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2026-05-28 11:59 |
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Revised |
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2026-06-08 14:53 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-07-20 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-07-20 06:57 |
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Articles in Press |
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2026-07-20 06:57 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-21 08:29 |
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Publish the Manuscript Online |
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2026-09-02 06:21 |
| ISSN |
2220-3230 (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 |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Transplantation |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence and machine learning in transplantation surgery care pathway
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Kavyesh Vivek and Vassilios Papalois |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Kavyesh Vivek, Department of Surgery and Cancer, Imperial College University, South Kensington Campus, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net |
| Key Words |
Artificial intelligence; Machine learning; Pre-operative planning; Peri-operative care; Rehabilitation |
| Core Tip |
Artificial intelligence (AI) and machine learning (ML) are transforming transplantation by enhancing precision across preoperative, perioperative, and postoperative phases. Deep learning improves anatomical assessment, graft evaluation, and candidate selection, while ML-based models predict intraoperative complications and postoperative risks such as sepsis, graft dysfunction, and renal injury. AI-assisted surgical platforms and multimodal predictive systems integrating imaging, histopathology, and electronic health records further personalise decision-making, optimise recovery, and refine long-term graft monitoring. Successful clinical translation, however, hinges on rigorous validation, diverse datasets, and robust ethical and regulatory oversight. |
| Publish Date |
2026-09-02 06:21 |
| Citation |
Vivek K, Papalois V. Artificial intelligence and machine learning in transplantation surgery care pathway. World J Transplant 2026; 16(3): 122433
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| URL |
https://www.wjgnet.com/2220-3230/full/v16/i3/122433.htm |
| DOI |
https://doi.org/10.5500/wjt.122433 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
