The role of artificial intelligence in predicting graft survival in kidney transplantation: a systematic review

INTRODUCTION: Gradient-boosting (GB) algorithm is considered as the state-of-the-art algorithm for prediction of survival. The aim of the current study was consolidating the evidence on GB machine-learning (ML) model to predict graft survival (GS) after kidney transplant (KT). EVIDENCE ACQUISITION: A systematic search (PROSPERO: CRD42025645353) with a qualitative analysis was performed according to PRISMA statement. Study quality and risk of bias were evaluated using the Prediction-model Risk of Bias ASsessment Tool (PROBAST). EVIDENCE SYNTHESIS: Overall, 15 studies involving 889,657 patients were included in the final analysis. Of those, 14,334 included GS information. According to ML algorithm, 12 (80.3%) studies relied on eXtremeGB, two (13.3%) on StochasticGB and one (6.4%) on lightGB. The model performance was evaluated with Area Under Curve (AUC) methodology in 12 (80%) of papers and ranged from 0.715 to 0.989. The Brier-score was evaluated in five (33.3%) papers and ranged from 0.020 to 0.14. The C-index and/or Accuracy were evaluated in three (20%) papers and ranged, in respectively, from 0.635 to 0.837, and from 0.81 to 0.979. CONCLUSIONS: The current systematic review showed a promising potential role of GB in the GS prediction after KT. However, ML models should be carefully interpreted before being used in clinical practice.