To explore the potential of artificial intelligence in predicting graft survival following kidney transplantation.
Conducted a systematic review of existing literature on AI in kidney transplantation.
Evaluated various machine learning models for their predictive accuracy.
Analyzed implications for clinical application of these models.
Artificial intelligence demonstrates promising potential in predicting graft survival outcomes.
Machine learning models require careful interpretation before clinical implementation.
Highlights the importance of validating AI predictions in real-world settings.
Abstract
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.