Mapping International Collaboration and Research Trends in Artificial Intelligence Applications for Liver and Kidney Transplantation

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Introduction: The integration of artificial intelligence (AI) in liver and kidney transplantation (LKT) research has surged in recent years, promising novel approaches to address traditional statistical challenges and enhance result robustness and generalizability. This study aims to explore the extent of international collaboration and the evolution of research trends in AI applications for LKT. Methods: On August 12, 2025, a systematic search was conducted using the Web of Science database to identify relevant literature. Bibliometric tools, including the "bibliometrix" package in R, VOSviewer, and Microsoft Excel were used. Key indicators such as country contributions, multiple-country publications, single-country publications, co-authorship, and keyword co-occurrence were examined to assess collaboration patterns and research hotspots. Inclusion criteria involved all published peer-reviewed articles related to AI in LKT. Editorials, corrections, and irrelevant documents were excluded. Results: A total of 633 articles published between 1994 and 2025 were included in the analysis. These collectively received 8959 citations. The United States of America emerged as the leading contributor, accounting for 37.12% of the publications, followed by China and South Korea. Notably, international co-authorship was evident in 30.02% of the publications. Keyword analysis revealed that "survival," "outcomes," "risk," "mortality," and "prediction" were the most frequent terms, highlighting them as hotspots in transplantation research. Conclusion: The field of AI in LKT research is characterized by a growing international collaboration, despite the fact that participation is still uneven and concentrated in high-income countries. In order to advance the field and enhance outcomes across diverse patient populations, it will be crucial to strengthen global data-sharing and cultivate equity-focused, culturally adaptable AI models.

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