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

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).

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).

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.

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.