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Machine learning in solid organ transplantation:Charting the evolving landscape

World Journal of Transplantation 2025年第1期15卷 165-177页

作者： Badi Rawashdeh Haneen Al-abdallat emre arpali Beje Thomas Ty B Dunn Matthew Cooper Division of Transplant Surgery Medical College of WisconsinMilwaukeeWI 53202United States Department of Medicine Jordan University HospitalAmman 11263Jordan Department of Nephrology Medical College of WisconsinMilwaukeeWI 53226United States

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 ***,the"Journal of Heart and Lung Transplantation"and the"American Journal of Transplantation"emerged 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

关键词： Machine learning Artificial Intelligence Solid organ transplantation Bibliometric analysis

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