The Role of Artificial Intelligence in The Diagnosis, Monitoring and Management of Anemia in CKD: A Systematic Review

Abstract

Background: Anemia has a major influence on patient morbidity and mortality and is a common consequence of chronic kidney disease (CKD). Optimizing treatment outcomes is frequently not possible with traditional management techniques. Artificial intelligence (AI) has emerged as a viable method for improving the diagnosis, monitoring, and management of anaemia in CKD patients. Objective: To conduct a systematic evaluation of the use and efficacy of AI technologies in the diagnosis, monitoring, and management of anemia in CKD patients. Methods: A complete literature search was undertaken using PubMed, Scopus, and Web of Science for research published between 2010 and 2025.The inclusion criteria included research that used AI methodologies, such as machine learning, deep learning, and predictive analytics—to treat anemia in CKD. Data were retrieved and processed according to PRISMA criteria. Results: In total, 54 studies met the inclusion criteria. AI models revealed improved diagnostic accuracy for anemia identification, outperforming traditional approaches in terms of sensitivity and specificity. AI enabled real-time monitoring of hemoglobin levels and erythropoiesis-stimulating agent (ESA) responses. Furthermore, AI-powered prediction models enhanced ESA dosing, lowering the number of adverse events related with anemia treatment. Conclusion: AI has the potential to revolutionize anemia therapy in CKD by offering precise diagnosis, continuous monitoring, and individualized treatment plans. However, additional large-scale, multicenter investigations are required to corroborate these findings and ease the adoption of AI tools into clinical practice.

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