Artificial intelligence in hemodialysis
Abstract:
Introduction: Timely adjustment of intervention strategies based on multidimensional hemodialysis data is essential for improving patients’ quality of life, enhancing clinical outcomes, and reducing complications. However, conventional management (relying heavily on clinician experience and intermittent monitoring) often fails to provide personalized, real-time, and proactive care. Efficient, data-driven methods are therefore urgently needed. In recent years, artificial intelligence (AI) has shown increasing potential in hemodialysis management, yet comprehensive reviews in this area remain limited.
Discussion: This narrative review synthesizes recent advances in AI applications for hemodialysis, examining their potential, technical approaches, and practical effectiveness in addressing current management challenges. We focus on five representative domains: hemodynamic management, volume management, dialysis adequacy assessment, vascular access management, and renal anemia prediction. Furthermore, we discuss emerging opportunities from wearable devices and multimodal data integration, while also highlighting major barriers to translation, particularly the gap between retrospective predictive performance and proven improvement in hard clinical outcomes. By offering theoretical insights and practical directions, this review aims to support the transition from experience-based care toward an AI-driven, data-informed paradigm, facilitating the development of intelligent, closed-loop decision-support systems that cover the entire dialysis journey.
Reference:
Ren H, Yang C, Xiong Z, Liang W, Su C, Yang G. Artificial Intelligence in Hemodialysis: Current Clinical Applications and Future Perspectives. Hemodial Int. 2026 Jul 2. doi: 10.1111/hdi.70110. Epub ahead of print. PMID: 42393005.