What does this research mean for the field?

Machine learning models have a promising potential role in predicting graft survival after kidney transplantation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

February 19, 2026Minerva Urology and Nephrology0 citations

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

FBFrancesco Di Bello OGOn behalf of the Members of the VivalDI -study and Dry Immersion expert group AGAndrea Gallioli

Key Points

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.

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Cite This Study

Bello et al. (Sun,) studied this question.

synapsesocial.com/papers/6996a7a5ecb39a600b3ed79e https://doi.org/https://doi.org/10.23736/s2724-6051.25.06559-0

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