Chronic kidney disease (CKD) represents a major and expanding global health challenge, with prevalence rising due to aging populations, diabetes, hypertension, and environmental factors. Conventional risk assessment tools such as the CKD Epidemiology Collaboration equation and the Kidney Failure Risk Equation are limited in precision, generalizability, and their ability to identify rapid progressors in early stages. This review examines the transformative role of artificial intelligence (AI), encompassing machine learning, deep learning, natural language processing, and multimodal data integration, in improving CKD detection, progression prediction, and personalized management. Drawing on recent evidence, we highlight AI's capacity to process high‑dimensional data from electronic health records, imaging, omics, and wearable devices, achieving area under the curve values of 0.85-0.96 for predicting outcomes, such as end‑stage kidney disease and therapeutic response. Key applications include early CKD screening using gradient boosting and long short‑term memory networks, biomarker discovery through multi‑omics fusion, and precision phenotyping to guide targeted interventions such as sodium-glucose cotransporter‑2 inhibitor therapy and optimized dialysis initiation. Persistent challenges algorithmic bias, data privacy, interpretability, and regulatory compliance-necessitate strategies such as federated learning, explainable AI, and ethically guided, equitable implementation.
Keywords: Artificial intelligence; Chronic kidney disease; Deep learning; Machine learning; Precision medicine; Risk prediction.
© 2025. The Author(s), under exclusive licence to Springer Nature B.V.