Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply Artificial Intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the United Kingdom’s transplant selection process. Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry (UKTR) database were analysed. The transplants were randomly divided into training (70%) and validation (30%) sets. Death-censored graft survival was the primary performance indicator. We experimented with four machine learning models assessed for calibration and discrimination (integrated Brier score (IBS), and Harrell’s concordance index). We assessed the potential clinical utility using decision curve analysis.

XGBoost demonstrated the best discriminative performance for survival (AUC=0.73, 0.74, and 0.75 at 3, 7, and 10 years post-transplant, respectively). The concordance index was 0.72. The calibration process was adequate, as evidenced by the IBS score of 0.09. By evaluating possible donor – recipient pairs based on graft survival, the AI-based UK-LTOP has the potential to enhance choices for the best live-donor selection. This methodology may improve the outcomes of kidney paired exchange schemes.   In general terms we show how the new AI and machine learning tools can have a role in developing effective and equitable healthcare.