The impact of artificial intelligence and machine learning in organ retrieval and transplantation - A comprehensive review

Aug 2, 2026

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This narrative review examines how artificial intelligence (AI) and machine learning (ML) reshape organ retrieval and transplantation across the full clinical continuum. AI-driven donor–recipient matching integrates clinical, genetic, and demographic data to improve organ allocation and transplant success. Image analysis supports automated segmentation and surgical outcome prediction, while predictive analytics forecast rejection, infection, and recovery trajectories to enable earlier intervention. Operational optimization in transplant centers includes demand forecasting, scheduling support, and inventory management to reduce wastage. Key barriers include privacy, regulatory requirements, system interoperability, and the need for rigorous clinical validation, with future directions emphasizing genomics integration, robotic minimally invasive surgery, and remote post-transplant monitoring.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

AI and ML for donor–recipient matching

AI-driven surgical planning and intraoperative support

Predictive analytics for post-operative care

Operational optimization in transplant centers

Challenges to adoption and validation

Future directions in AI-enabled transplantation

Current Research in Translational Medicine 73 (2025) 103493 Contents lists available at ScienceDirect Current Research in Translational Medicine journal [homepage: www.elsevier.com/locate/retram](homepage: www.elsevier.com/locate/retram) | General review
The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review | | | | | --- | --- | --- | --- | | David B. Olawade a,b,c,d,* , Sheila Marinzee, Nabeel Qureshi e, Kusal Weerasingheb, Jennifer Tekeb,f
a Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom b Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
c Department of Public Health, York St John University, London, United Kingdom
d School of Health and Care Management, Arden University, Arden House, Middlemarch Park, Coventry CV3 4FJ, United Kingdome Department of Surgery, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
f Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Machine learning Organ transplantation Donor-recipient matching Surgical planning Healthcare optimization | | This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding preoperative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency. | | 1. Introduction Organ transplantation stands as a cornerstone of modern medicine, offering life-saving treatments for patients facing end-stage organ failure [1]. Despite significant advancements in surgical techniques and immunosuppressive therapies, the demand for donor organs far outweighs their supply, leading to prolonged waiting times and increased mortality rates among transplant candidates [2]. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into organ retrieval and transplantation processes represents a promising avenue to address these challenges and enhance the efficiency an

FAQs

How do AI and ML improve donor-recipient matching in organ transplantation?

They integrate and analyze complex clinical, genetic, and demographic datasets to support more precise organ allocation than traditional criteria alone.

What role does AI play in surgical planning during transplantation?

AI-driven image analysis can automate organ segmentation, highlight critical anatomical features, and predict surgical outcomes to improve preoperative planning and reduce intraoperative risk.

What challenges limit the widespread adoption of AI and ML in transplantation medicine?

Data privacy concerns, regulatory compliance, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models are key barriers.

Aug 2, 2026

Upload by Liam