Publication Name	World Journal of Transplantation
Manuscript ID	99642
Country	United States

Received	2024-08-14 02:19
Peer-Review Started	2024-07-26 17:39
To Make the First Decision
Return for Revision	2024-09-27 12:36
Revised	2024-10-17 20:46
Second Decision	2024-11-06 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-11-06 08:50
Articles in Press	2024-11-06 08:50
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-11-15 03:40
Publish the Manuscript Online	2024-11-27 08:57

ISSN	2220-3230 (online)
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/
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Category	Transplantation
Manuscript Type	Scientometrics
Article Title	Machine learning in solid organ transplantation: Charting the evolving landscape
Manuscript Source	Invited Manuscript
All Author List	Badi Rawashdeh, Haneen Al-abdallat, Emre Arpali, Beje Thomas, Ty B Dunn and Matthew Cooper
ORCID	Author(s) ORCID Number Badi Rawashdeh http://orcid.org/0000-0003-4193-4654 Matthew Cooper http://orcid.org/0000-0002-3438-9638
Funding Agency and Grant Number
Corresponding Author	Badi Rawashdeh, Assistant Professor, Division of Transplant Surgery, Medical College of Wisconsin, No. 9200 wisconsin ave milwaukee, Milwaukee, WI 53202, United States. brawashdeh@mcw.edu
Key Words	Machine learning; Artificial Intelligence; Solid organ transplantation; Bibliometric analysis
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.
Publish Date	2024-11-27 08:57
Citation	<p>Rawashdeh B, Al-abdallat H, Arpali E, Thomas B, Dunn TB, Cooper M. Machine learning in solid organ transplantation: Charting the evolving landscape. <i>World J Transplant</i> 2025; 15(1): 99642</p>
URL	https://www.wjgnet.com/2220-3230/full/v15/i1/99642.htm
DOI	https://dx.doi.org/10.5500/wjt.v15.i1.99642

Full Article (PDF)	WJT-15-99642-with-cover.pdf
PRISMA 2009 Checklist	99642-PRISMA-2009-Checklist.pdf
Manuscript File	99642_Auto_Edited_035132.docx
Answering Reviewers	99642-answering-reviewers.pdf
Audio Core Tip	99642-audio.m4a
Biostatistics Review Certificate	99642-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	99642-conflict-of-interest-statement.pdf
Copyright License Agreement	99642-copyright-assignment.pdf
Peer-review Report	99642-peer-reviews.pdf
Scientific Misconduct Check	99642-scientific-misconduct-check.png
Scientific Editor Work List	99642-scientific-editor-work-list.pdf
CrossCheck Report	99642-crosscheck-report.pdf