Clinical Report: Trends and Future Perspectives of AI in Chronic Kidney Disease
Overview
This bibliometric analysis of 691 publications from 2005 to 2025 highlights the rapid growth and evolving research landscape of artificial intelligence (AI) applications in chronic kidney disease (CKD). The study identifies key contributors, research hotspots, and thematic trends, underscoring AI's expanding role in CKD diagnosis, treatment, and prognosis.
Background
Chronic kidney disease (CKD) is a significant global health challenge often diagnosed late due to asymptomatic early stages, necessitating improved screening and personalized treatment strategies. Artificial intelligence (AI) technologies, including machine learning and deep learning, have shown promise in enhancing CKD diagnosis, biomarker discovery, and treatment planning. Despite increasing research interest, a comprehensive synthesis of AI's impact on CKD has been lacking. Bibliometric analysis offers a systematic approach to map the knowledge structure and forecast future research directions in this dynamic field.
AI research in CKD has grown exponentially, especially since 2017, reflecting increased academic and clinical interest.
Early detection and personalized treatment of CKD remain challenging, with AI offering potential solutions through advanced data processing and predictive modeling.
Bibliometric tools identified core journals, influential authors, and collaborative networks shaping the AI-CKD research landscape.
Emerging AI applications include multimodal pathology assistance, imaging diagnostics, biomarker discovery, and protein structure prediction relevant to CKD.
Despite advances, routine CKD screening compliance remains low, highlighting the need for AI-driven accessible diagnostic tools.
Clinical Implications
The integration of AI into CKD management can facilitate earlier diagnosis and tailored treatment strategies, potentially improving patient outcomes. Clinicians should stay informed about AI advancements to leverage these technologies for enhanced screening and prognostic assessments. Additionally, fostering interdisciplinary collaboration may accelerate the translation of AI research into clinical practice.
Conclusion
This comprehensive bibliometric analysis underscores the transformative potential of AI in CKD research and clinical care. Continued growth and collaboration in this field are expected to drive innovations that improve early detection and personalized management of CKD.
Related Resources & Content
W.Q.Y et al. 2025 -- Exploring Trends and Future Perspectives of Artificial Intelligence in Chronic Kidney Disease: A Bibliometric and Visual Analysis
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