Vivek K, Papalois V. Artificial intelligence and machine learning in transplantation surgery care pathway. World J Transplant 2026; 16(3): 122433 [DOI: 10.5500/wjt.122433]
Corresponding Author of This Article
Kavyesh Vivek MBBS, BSc, Department of Surgery and Cancer, Imperial College University, Imperial College London South Kensington Campus, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net
Research Domain of This Article
Transplantation
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review-article
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Vivek K, Papalois V. Artificial intelligence and machine learning in transplantation surgery care pathway. World J Transplant 2026; 16(3): 122433 [DOI: 10.5500/wjt.122433]
World J Transplant. Sep 18, 2026; 16(3): 122433 Published online Sep 18, 2026. doi: 10.5500/wjt.122433
Artificial intelligence and machine learning in transplantation surgery care pathway
Kavyesh Vivek, Vassilios Papalois
Kavyesh Vivek, Department of Surgery and Cancer, Imperial College University, London SW7 2AZ, United Kingdom
Vassilios Papalois, Directorate of Renal and Transplant Services, London W12 OHS, United Kingdom
Co-first authors: Kavyesh Vivek and Vassilios Papalois.
Author contributions: Vivek K was primarily responsible for conducting the research, analysing the data, and writing the manuscript; Papalois V provided guidance throughout the research process, contributed to the conceptual development of the study, and offered critical revisions and supervision.
AI contribution statement: AI was not used to generate, translate, or structurally organise this manuscript. No AI tools were used for literature organisation, reference formatting, figure or table generation, raw data analysis, or statistical analyses. No section of the manuscript — including the abstract, introduction, methods, results, discussion, conclusion, or references — was generated using AI. AI-assisted language editing and proofreading tools were used during manuscript preparation. All intellectual content, clinical interpretation, and final editorial decisions remain the sole responsibility of the named authors. No AI system is listed as an author or co-author.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Kavyesh Vivek MBBS, BSc, Department of Surgery and Cancer, Imperial College University, Imperial College London South Kensington Campus, London SW7 2AZ, United Kingdom. kavyesh.vivek2@nhs.net
Received: April 20, 2026 Revised: June 8, 2026 Accepted: July 20, 2026 Published online: September 18, 2026 Processing time: 133 Days and 16.5 Hours
Core Tip
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.
