The role of artificial intelligence in predicting graft survival in kidney transplantation: a systematic review
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.
Authors
- A. Piana
- Francesco Di Bello (ORCID: https://orcid.org/0000-0002-8585-3701)
- P. Gavrilov
- JOSEP GAYÀ
- Ángelo Territo
- S. Mancon
- Muhammet İrfan Dönmez (ORCID: https://orcid.org/0000-0002-2828-7942)
- Hakan Bahadır Haberal (ORCID: https://orcid.org/0000-0001-9774-2040)
- Donato Cannoletta
- B. Bañuelos Marco
- L L Guirado
- Alicia López‐Abad
- Alessio Pecoraro
- C. Facundo
- A. Kanashiro
- Alberto Breda
- Andrea Gallioli
- On behalf of the Members of the VivalDI -study and Dry Immersion expert group
- Thomas PRUDHOMME
- Oscar RODRIGUEZ-FABA
Institutions
- Ministry of Health (TR)
- Humanitas University (IT)
- Hospital Clínico San Carlos (ES)
- Ankara Atatürk Eğitim ve Araştırma Hastanesi (TR)
- Hôpital Rangueil (FR)
- Sağlık Bilimleri Üniversitesi (TR)
- IRCCS Ospedale San Raffaele (IT)
- Puigvert Foundation (ES)
- Hospital Universitario Virgen de la Arrixaca (ES)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Ospedale San Luigi Gonzaga (IT)
- University of Florence (IT)
- University of Turin (IT)
- Istanbul University (TR)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Minerva Urology and Nephrology
- Published
- 2026-02-01
- DOI
- https://doi.org/10.23736/s2724-6051.25.06559-0
- Primary Topic
- Renal Transplantation Outcomes and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00