Exploring Trends and Future Perspectives of Artificial Intelligence in Chronic Kidney Disease: A Bibliometric and Visual Analysis - Scorecard - MDSpire
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Exploring Trends and Future Perspectives of Artificial Intelligence in Chronic Kidney Disease: A Bibliometric and Visual Analysis
Clinical Scorecard: Exploring Trends and Future Perspectives of Artificial Intelligence in Chronic Kidney Disease: A Bibliometric and Visual Analysis
At a Glance
Category
Detail
Condition
Chronic Kidney Disease (CKD)
Key Mechanisms
AI technologies including data processing, image recognition, automation, and predictive modeling applied to CKD diagnosis, treatment strategies, and prognosis prediction
Target Population
General population and high-risk groups such as individuals with diabetes or hypertension
Care Setting
Clinical and research settings involving nephrologists and healthcare professionals
Key Highlights
CKD is often asymptomatic in early stages, hindering early detection and timely intervention.
AI applications in CKD include screening via imaging diagnostics, biomarker discovery, and personalized treatment decision support.
Bibliometric analysis reveals rapid growth in AI-related CKD research, with increasing publications and expanding academic collaboration networks.
Guideline-Based Recommendations
Diagnosis
Regular screening of high-risk populations using serum or urine samples is recommended but limited by low compliance.
AI-assisted imaging diagnostics and biomarker identification can facilitate earlier and more accurate CKD detection.
Management
Personalized treatment strategies should be developed by experienced physicians, potentially supported by AI decision tools.
Integration of AI technologies can enhance treatment planning and disease progression prediction.
Monitoring & Follow-up
Ongoing monitoring of CKD progression can be improved by AI predictive models and automated data analysis.
Risks
Low compliance with traditional screening methods may delay diagnosis.
Complexity of CKD management necessitates expert interpretation of AI outputs to avoid mismanagement.
Patient & Prescribing Data
Patients with CKD and those at high risk due to comorbidities like diabetes and hypertension
AI technologies can support personalized treatment decisions and improve prognosis prediction, potentially enhancing patient outcomes.
Clinical Best Practices
Implement regular CKD screening in high-risk populations to enable early detection.
Utilize AI-assisted diagnostic tools to complement traditional methods and improve accuracy.
Engage multidisciplinary teams including nephrologists and AI specialists for personalized treatment planning.
Adopt bibliometric insights to stay informed on emerging AI applications and research trends in CKD.
