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Artificial intelligence in chronic kidney disease management: a scoping review
Charumathi Sabanayagam1,2,3, Riswana Banu1, Cynthia Lim4
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Theranostics
|April 14, 2025
Summary
Artificial intelligence (AI) shows great promise for improving chronic kidney disease (CKD) management through early detection and personalized treatments. Addressing challenges in data, accuracy, and integration is key to realizing AI
Area of Science:
- Nephrology
- Medical Informatics
- Artificial Intelligence
Background:
- Chronic kidney disease (CKD) presents a significant global health challenge, necessitating advanced management strategies.
- Existing approaches require innovation to mitigate the rising burden of CKD, including its associated cardiovascular disease, renal failure, and mortality risks.
Purpose of the Study:
- To conduct a scoping review identifying artificial intelligence (AI) applications for enhancing CKD management.
- To examine challenges and propose solutions for AI integration in CKD care.
Main Methods:
- A systematic review of 41 articles (2014-2024) focusing on AI techniques like machine learning (ML), deep learning (DL), natural language processing (NLP), and large language models (LLMs).
- Analysis centered on four key areas: early detection, risk stratification, treatment recommendations, and patient communication.
Main Results:
- AI applications identified include DL for early detection (6 studies), ML for risk stratification/prediction (14 studies) and treatment recommendations (4 studies), and NLP/LLMs for patient care (17 studies).
- Key challenges involve data quality, model accuracy, interpretability, workflow integration, user training, and regulatory hurdles.
Conclusions:
- AI holds significant potential to improve early detection, prediction, and patient outcomes in CKD management.
- Multidisciplinary collaboration is essential for developing safe, compliant, and effective AI integration protocols in clinical practice.
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