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
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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 4.6 Hours
Abstract
Artificial intelligence (AI) and machine learning (ML) are increasingly applied across the transplantation pathway, offering advances in preoperative planning, perioperative management, and postoperative recovery. In preoperative care, deep learning algorithms improve anatomical assessment, volumetry, and graft weight estimation, while ML-based functional status evaluation and urgency scoring refine candidate selection. Predictive models incorporating metabolic and physiological data further support surgical eligibility and targeted prehabilitation strategies. Perioperatively, ML models outperform conventional approaches in predicting massive transfusion, intraoperative haemorrhage, and acute kidney injury, with explainable outputs enhancing interpretability and clinical trust. Robotic and AI-assisted surgical platforms demonstrate functional equivalence or superiority to conventional methods, reducing intraoperative complications and accelerating recovery, particularly in high-risk cohorts. Postoperatively, ML-driven models enable early prediction of sepsis, pneumonia, and graft dysfunction, while longitudinal markers such as the recipient-to-donor estimated glomerular filtration rate ratio and novel imaging or biomarker-based approaches inform long-term graft monitoring. Optimised perioperative strategies, including analgesic regimens and fluid management, further enhance donor recovery and rehabilitation outcomes. Cross-cutting innovations include imaging-based AI applications such as hyperspectral imaging for real-time graft viability assessment and deep learning for automated histopathological evaluation, which improve diagnostic speed, accuracy, and reproducibility. Multimodal models integrating electronic health records, intraoperative signals, ultrasound, and histology provide dynamic, system-wide insights into graft function and rejection risk, bridging diagnostic, prognostic, and therapeutic decision-making. AI and ML thus hold substantial potential to personalise transplant care and improve outcomes. Their translation into practice, however, requires rigorous validation, dataset diversity, and strong ethical and regulatory governance.
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.
