Perspectives of Artificial Intelligence Enabled Diagnosis and Treatment Management of Cardiorenal Syndrome

Cardiorenal syndrome (CRS) encompasses a spectrum of disorders arising from bidirectional interactions between the heart and kidneys and is associated with substantial morbidity, mortality, and healthcare burden. Early recognition remains challenging because conventional biomarkers and imaging modalities may inadequately detect subclinical disease or distinguish functional changes from true organ injury. This narrative review examines the epidemiology, classification, pathophysiology, diagnostic approaches, and contemporary management of CRS, with particular emphasis on the emerging role of artificial intelligence (AI). Relevant evidence regarding established and emerging diagnostic and therapeutic approaches was reviewed, including biomarkers, imaging, guideline-directed medical therapy, ultrafiltration, cardiac devices, transplantation, and AI-based applications. Current evidence demonstrates the multifactorial nature of CRS and highlights the importance of early diagnosis and individualized management. Machine learning and deep learning approaches have shown potential to integrate electronic health records, biomarkers, imaging, physiological signals, and multi-omics data to support early detection, risk stratification, outcome prediction, and therapeutic decision-making. AI-assisted approaches may complement conventional diagnostic and management strategies and facilitate more precise and proactive CRS care. However, limitations related to data quality, model interpretability, external validation, and clinical implementation must be addressed before widespread clinical adoption.