ArticleDigital health
Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What Socratic 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 Socratic graph.