Scientometrics
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Transplant. Mar 18, 2025; 15(1): 99642
Published online Mar 18, 2025. doi: 10.5500/wjt.v15.i1.99642
Machine learning in solid organ transplantation: Charting the evolving landscape
Badi Rawashdeh, Haneen Al-abdallat, Emre Arpali, Beje Thomas, Ty B Dunn, Matthew Cooper
Badi Rawashdeh, Emre Arpali, Ty B Dunn, Matthew Cooper, Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States
Haneen Al-abdallat, Department of Medicine, Jordan University Hospital, Amman 11263, Jordan
Beje Thomas, Department of Nephrology, Medical College of Wisconsin, Milwaukee, WI 53226, United States
Author contributions: Rawashdeh B conducted the literature review and bibliometric analysis; Arpali E contributed to the bibliometric analysis; Dunn TB and Cooper M were responsible for editing, reviewing, and supervision; Al-abdallat H and Thomas B contributed as co-authors with editing and literature review. All authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that there is no conflict of interest regarding the publication of this manuscript.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Badi Rawashdeh, Doctor, Transplant Surgeon, Division of Transplant Surgery, Medical College of Wisconsin, 9200 Wisconsin Ave Milwaukee, Milwaukee, WI 53226, United States. brawashdeh@mcw.edu
Received: July 26, 2024
Revised: October 17, 2024
Accepted: November 6, 2024
Published online: March 18, 2025
Processing time: 123 Days and 17.4 Hours
Core Tip

Core Tip: Machine learning (ML) is transforming solid organ transplantation by improving donor-recipient matching, post-transplant monitoring, and patient care via advanced data analysis and outcome forecasting. This bibliometric analysis of 427 relevant publications shows a significant increase in interest and research, especially since 2018, with the United States leading the way. Key themes include patient survival, mortality, outcomes, allocation, and risk assessment, demonstrating ML's promising ability to transform medical practices and improve patient outcomes in transplantation. Collaboration among key contributors is critical for accelerating progress in this interdisciplinary field.