ReviewNPJ digital medicine2022
The promise of machine learning applications in solid organ transplantation.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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
42 citing papers in PubMed.
- Trial
- Leveraging machine learning to predict de novo skin malignancy following lung transplantation.Lung cancer management · 2026Article
- Leveraging Natural Language Processing to Identify Variation in Kidney Transplant Access.Kidney international reports · 2026Article
- Article
- Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering.World journal of transplantation · 2026Article
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Advances in HLA and Non-HLA Immune Profiling: Integrating Next-Generation Sequencing and Artificial Intelligence in Transplantation.Annals of laboratory medicine · 2026Review
- A jackknife approach to estimate the prediction uncertainty from binary classifiers under right-censoring.Statistical methods in medical research · 2026Article
- A data-driven framework for fair and efficient organ transplantation using gradient boosting and adaptive genetic allocation.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Article
- Review
- Review
- Article
- Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.BMC nephrology · 2025Article
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis.Transplantation direct · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers.Frontiers in artificial intelligence · 2025Article
- Using Unsupervised Clustering to Characterize Phenotypes Among Older Kidney Transplant Recipients: A Cohort Study.Canadian journal of kidney health and disease · 2025Article
- Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
Identifiers
What Socratic 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 Socratic graph.