To conduct a comprehensive bibliometric analysis of literature on AI applications in chronic kidney disease (CKD) to identify trends, research hotspots, and future directions, emphasizing the significance of these findings for clinical practice.
Approach:
Key Findings:
691 publications related to AI applications in CKD were identified from 2005 to 2025, indicating a growing body of research.
The publication volume showed three phases: initial (2005-2016), rapid expansion (2017-2021), and sustained growth (2022-2025), suggesting increasing interest and investment in this area.
89.9% of the publications were original research articles, highlighting the focus on novel contributions to the field.
Interpretation:
The rapid increase in publications indicates growing interest and advancements in AI applications for CKD diagnosis and treatment, highlighting the potential for improved patient outcomes through more accurate and timely interventions.
Limitations:
The study is limited to English-language publications, which may overlook significant research published in other languages.
Only literature from the WoSCC database was analyzed, potentially excluding relevant studies from other sources.
Conclusion:
The bibliometric analysis provides a panoramic reference for nephrologists and researchers, facilitating the clinical translation and adoption of AI technologies in CKD management, which is crucial for improving patient care.
Most surgeons reported using intraoperative parathyroid hormone monitoring, but approaches to imaging and intraoperative criteria varied, particularly in secondary and tertiary disease
