Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects.

Abstract

Background: Artificial intelligence (AI) has made significant advances in nephrology, revolutionizing the diagnosis, prognosis, and treatment of kidney diseases.

Summary: This review provides an overview of AI applications in nephrology, introducing the basic structures of each model, highlighting both traditional machine-learning approaches and neural networks, and providing model application comparisons along with selection recommendations. It discussed key challenges in deciding appropriate AI models for specific tasks and evaluated their advantages, limitations, and optimal use cases. Current applications of AI in nephrology mainly include diagnosis and disease outcome prediction, medical image analysis, treatment recommendations, and personalized health management, supported by massive electronic health records and multimodal data integration. Traditional machine learning models perform well on datasets of varying sizes and structures, while neural networks excel at handling complex and imaging data. Emerging hardware innovations are expected to improve the performance of neural network models, enabling more accurate diagnosis and automated analysis in clinical practice. In the future, AI will have great potential to advance individualized patient care and enable real-time data processing in nephrology.

Key messages: An overview of AI applications in nephrology is provided in this review.