Publication Name	World Journal of Transplantation
Manuscript ID	122433
DOI	10.5500/wjt.122433
Country	United Kingdom

Category	Transplantation
Manuscript Type	Minireviews
Article Title	Artificial intelligence and machine learning in transplantation surgery care pathway
Manuscript Source	Invited Manuscript
All Author List	Kavyesh Vivek and Vassilios Papalois
Funding Agency and Grant Number
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.
Citation	Vivek K, Papalois V. Artificial intelligence and machine learning in transplantation surgery care pathway. World J Transplant 2026; In press DOI: https://doi.org/10.5500/wjt.122433
PDF	122433-in-press.pdf

Received	2026-04-20 08:30
Peer-Review Started	2026-04-20 08:31
First Decision by Editorial Office Director	2026-05-28 11:59
Return for Revision	2026-05-28 11:59
Revised	2026-06-08 14:53
Publication Fee Transferred
Second Decision by Editor	2026-07-20 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-07-20 06:57
Articles in Press	2026-07-20 06:57
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-3230 (online)
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