Artificial Intelligence in Renal Transplantation Over the Past Decade: A Narrative Review of Clinical Applications, Current Limitations, and Future Directions

This narrative review examines the use of artificial intelligence (AI) and machine learning (ML) in kidney transplantation (KT) during the past 10 years, highlighting advancements in clinical applications and future potential. In pretransplant settings, AI algorithms assist in matching donors with recipients and predicting survival outcomes, aiming to reduce organ discard rates and improve allocation efficiency beyond traditional scoring systems like the Kidney Donor Profile Index. Surgical data science utilizes AI to enhance robotic surgery through augmented reality for real-time anatomical visualization and 3D printed models for preoperative planning. Furthermore, ML is applied to assess organ quality during normothermic machine perfusion. Regarding post-transplant outcomes, artificial neural networks have demonstrated superior accuracy in predicting graft survival and rejection compared to conventional statistical methods. Despite these advancements, clinical application is hindered by limitations such as overfitting, selection bias from single-center data, and a lack of external validation.