ReviewFrontiers in immunology2021
Machine Learning Applications in Solid Organ Transplantation and Related Complications.
Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.BMC medical research methodology · 2022Pooled it
- Tacrolimus dosing in liver transplant recipients using phenotypic personalized medicine: A phase 2 randomized clinical trial.Nature communications · 2025Trial
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Mapping International Collaboration and Research Trends in Artificial Intelligence Applications for Liver and Kidney Transplantation.Journal of transplantation · 2026Article
- From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.Frontiers in transplantation · 2026Article
- Assessing knowledge and perception of artificial intelligence in solid organ transplantation.Frontiers in digital health · 2026Article
- LCN2 drives ferroptosis-associated ischemia-reperfusion injury after renal transplantation: integrated machine learning and in vivo validation.Apoptosis : an international journal on programmed cell death · 2025Article
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
- A Comprehensive Review of Liver Allograft Fibrosis and Steatosis: From Cause to Diagnosis.Transplantation direct · 2023Review
- A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.BMC medical informatics and decision making · 2023Article
- Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2022Review
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
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.