ReviewCureus2025
Artificial Intelligence in Renal Transplantation Over the Past Decade: A Narrative Review of Clinical Applications, Current Limitations, and Future Directions.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Augmented reality in robotic urology: from 3D printed models to cognitive surgical systems. A systematic literature review.Journal of robotic surgery · 2026Pooled it
- Sequential antibody induction for immune tolerance in clinical organ transplantation: a feasibility review of immunosuppressant withdrawal protocols.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.