The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review.
Journal article
Olawade, David B, Marinze, Sheila, Qureshi, Nabeel, Weerasinghe, Kusal and Teke, Jennifer 2025. The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review. Current Research in Translational Medicine. 73 (2), p. 103493. https://doi.org/10.1016/j.retram.2025.103493
| Authors | Olawade, David B, Marinze, Sheila, Qureshi, Nabeel, Weerasinghe, Kusal and Teke, Jennifer |
|---|---|
| Abstract | 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 pre-operative 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. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.] |
| Keywords | Machine learning; Surgical planning; Organ transplantation; Donor-recipient matching; Healthcare optimization |
| Year | 2025 |
| Journal | Current Research in Translational Medicine |
| Journal citation | 73 (2), p. 103493 |
| Publisher | Elsevier |
| ISSN | 2452-3186 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.retram.2025.103493 |
| Official URL | https://www.sciencedirect.com/science/article/pii/S2452318625000029 |
| Publication dates | |
| Online | 06 Jan 2025 |
| Publication process dates | |
| Accepted | 05 Jan 2025 |
| Deposited | 23 Jan 2025 |
| Publisher's version | License File Access Level Open |
| Output status | Published |
| Additional information | Publications router. |
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https://repository.canterbury.ac.uk/item/9q247/the-impact-of-artificial-intelligence-and-machine-learning-in-organ-retrieval-and-transplantation-a-comprehensive-review
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