ReviewHepatology international2022
Application of machine learning in liver transplantation: a review.
Review in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Graft rejection across solid organ transplants: mechanisms, monitoring, and immunosuppressive therapeutics.Frontiers in surgery · 2026Pooled it
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory study.BMC gastroenterology · 2026Article
- Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.Frontiers in immunology · 2026Review
- GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.Nature communications · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- Machine Learning-Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.Journal of medical Internet research · 2025Article
- Article
- Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.Frontiers in artificial intelligence · 2025Article
- Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation.Mayo Clinic proceedings. Digital health · 2024Article
- Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review.Tomography (Ann Arbor, Mich.) · 2023Article
- Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma.Annals of translational medicine · 2023Article
- Revisiting transplant immunology through the lens of single-cell technologies.Seminars in immunopathology · 2023Review
- Review
- The promise of artificial intelligence for predictive biomarkers in hepatology.Hepatology international · 2022Article
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk.
aimIn this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation.
methodsTwenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials.
conclusionML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
Indexed as
Identifiers
35020154What 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.