Artificial intelligence and machine learning in transplantation surgery care pathway

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.