ArticleJournal of transplantation2026
Mapping International Collaboration and Research Trends in Artificial Intelligence Applications for Liver and Kidney Transplantation.
Article in Journal of transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Mapping International Collaboration and Research Trends in Artificial Intelligence Applications for Liver and Kidney Transplantation.Journal of transplantation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.