The integration of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidney Disease (CKD) is revolutionizing nephrology by providing enhanced diagnostic accuracy and improved patient outcomes. This research explores the transformative role of AI, particularly machine learning (ML) and deep learning (DL), in predicting CKD progression and facilitating timely interventions. AI-driven models analyze diverse patient data, including imaging, laboratory results, and genetic information, to identify subtle patterns often overlooked by traditional methods. These technologies allow for earlier identification of kidney dysfunction, potentially slowing disease progression and increasing life expectancy. The study highlights the importance of using ensemble learning techniques and feature selection methods to refine AI models, improving their predictive capabilities. Furthermore, the research emphasizes the potential of AI to support clinical decision-making by offering objective, data-driven risk assessments, which are crucial in the personalized management of CKD. The use of convolutional neural networks (CNNs) in renal imaging has shown promise in detecting early-stage kidney damage, while support vector machines (SVM) and artificial neural networks (ANN) have demonstrated high accuracy in diagnosing CKD. The growing integration of AI into healthcare workflows is expected to reduce diagnostic delays, enhance prognostic evaluations, and optimize treatment strategies. This study also discusses the interdisciplinary collaboration between medicine, computer science, and engineering, which is essential for advancing AI applications in CKD. With the increasing availability of high-quality data and computational tools, AI is poised to play a central role in transforming CKD management, offering a proactive approach to care that can lead to better patient outcomes and healthcare resource efficiency.

The authors have declared no competing interest.

This study did not receive any funding.

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