Rawashdeh B, Al-abdallat H, Arpali E, Thomas B, Dunn TB, Cooper M. Machine learning in solid organ transplantation: Charting the evolving landscape. World J Transplant 2025; 15(1): 99642 [DOI: 10.5500/wjt.v15.i1.99642]
Corresponding Author of This Article
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
Research Domain of This Article
Transplantation
Article-Type of This Article
Scientometrics
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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
Abstract
BACKGROUND
Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.
AIM
To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.
METHODS
On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.
RESULTS
Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.
CONCLUSION
The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.
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.
