SynthesisArtificial organs2022
Machine learning and artificial intelligence in cardiac transplantation: A systematic review.
Synthesis in Artificial organs, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.BMC cardiovascular disorders · 2024Pooled it
- Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.BMC medical research methodology · 2022Pooled it
- Machine learning and artificial intelligence in cardiac transplantation: A systematic review.Artificial organs · 2022Pooled it
- XGBoost Outperforms Traditional Transplant Regulatory Models Based on Regression.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Reperfusion Strategies in Donation After Circulatory Death Heart Transplantation: An Up-to-Date Narrative Review.Journal of clinical medicine · 2026Review
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study.Annals of medicine and surgery (2012) · 2025Review
- Survival analysis using machine learning in transplantation: a practical introduction.BMC medical informatics and decision making · 2025Article
- The ethical considerations of integrating artificial intelligence into surgery: a review.Interdisciplinary cardiovascular and thoracic surgery · 2025Review
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Predicting Factors Affecting Survival Rate in Patients Undergoing On-Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review.Health science reports · 2025Review
- Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.PloS one · 2025Article
- A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.Frontiers in artificial intelligence · 2025Review
- Immunocompatibility in transplantation: adapting to a changing therapeutic landscape.Frontiers in immunology · 2025Review
- Use of regulatory cells for achieving functional tolerance of pig heart xenotransplants in humans: a literature review.Frontiers in immunology · 2025Review
- The molecular mechanisms of cardiac development and related diseases.Signal transduction and targeted therapy · 2024Review
- Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation.
methodsA systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021.
resultsOur search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk.
conclusionML demonstrated promising applications for improving heart transplantation outcomes and patient-centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.
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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.