Machine learning in solid organ transplantation:Charting the evolving landscape

作     者：Badi Rawashdeh Haneen Al-abdallat Emre Arpali Beje Thomas Ty B Dunn Matthew Cooper 

作者机构：Division of Transplant SurgeryMedical College of WisconsinMilwaukeeWI 53202United States Department of MedicineJordan University HospitalAmman 11263Jordan Department of NephrologyMedical College of WisconsinMilwaukeeWI 53226United States 

出 版 物：《World Journal of Transplantation》 (世界移植杂志（英文）)

年 卷 期：2025年第15卷第1期

页      面：165-177页

学科分类：1002[医学-临床医学] 100210[医学-外科学(含：普外、骨外、泌尿外、胸心外、神外、整形、烧伤、野战外)] 10[医学] 

主　　题：Machine learning Artificial Intelligence Solid organ transplantation Bibliometric analysis 

摘      要：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 *** provides revolutionary opportunities in areas such as donorrecipient matching,post-transplant monitoring,and patient care by automatically analyzing large amounts of data,identifying patterns,and forecasting *** To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their *** On July 18,a thorough search strategy was used with the Web of Science *** and transplantation-related keywords were *** 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 *** 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 *** 209 publications,the United States emerged as the top ***,theJournal of Heart and Lung Transplantationand theAmerican Journal of Transplantationemerged as the leading journals,publishing the highest number of relevant *** keyword searches revealed that patient survival,mortality,outcomes,allocation,and risk assessment were significant themes of *** The growing body of pertinent publications highlights ML s growing presence in the field of solid organ *** bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient *** collaboration between signific