Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions

Background Artificial intelligence (AI) applications in medicine are rapidly expanding, revolutionizing the field of Chronic Kidney Disease (CKD) through diagnostics, prognosis prediction, and treatment decision-making. Despite significant progress, systematic analyses integrating AI with CKD remain limited. Methods Literature included in this study was sourced from the Web of Science Core Collection. Using tools such as CiteSpace, VOSviewer, and R-Bibliometrix, 888 relevant publications were analyzed. Research data encompassed dimensions including annual publication trends, author influence, institutional contributions, national output, keywords, and co-citation evolution. Results Research on AI and CKD has experienced exponential growth, particularly since 2019. China and the United States dominate paper publications, with leading institutions including the Sun Yat-sen University and University of California System. Core authors focus on AI-driven non-invasive biomarkers and CKD diagnosis via histopathological images. Global research trends shift from traditional machine learning to deep learning, emphasizing digital pathology and multimodal models to improve diagnostic and prognostic outcomes. Conclusion Between 2019 and 2025, the number of related publications grew rapidly, accelerating AI-driven advancements in CKD. Research emphasis has evolved from initial exploratory studies to clinically oriented applications centered on “deep learning models for image analysis, disease diagnosis, outcome prediction, and multimodal data integration.” Future efforts should prioritize integrating multi-omics technologies into multimodal models and developing fully automated hybrid models to advance AI from diagnostic support to clinical decision-making. These insights provide direction for future AI-driven innovations, promising enhanced precision in CKD management and improved patient outcomes.