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Artificial Intelligence in Hemodialysis: Current Clinical Applications and Future Perspectives.
Hui Ren1, Cong Yang2, Zibo Xiong1
1Division of Renal Medicine, Peking University Shenzhen Hospital, Peking University, Shenzhen, China.
Hemodialysis International. International Symposium on Home Hemodialysis
|July 2, 2026
Summary
Artificial intelligence (AI) offers promising solutions for hemodialysis management, moving beyond traditional methods. This review explores AI applications to enhance patient care and clinical outcomes in hemodialysis.
Area of Science:
- Nephrology
- Medical Informatics
- Artificial Intelligence
Background:
- Current hemodialysis management relies on clinician experience and intermittent monitoring, often failing to provide personalized, real-time care.
- Data-driven methods are needed to improve quality of life, clinical outcomes, and reduce complications in hemodialysis patients.
- Artificial intelligence (AI) shows potential in hemodialysis management, but comprehensive reviews are limited.
Purpose of the Study:
- To synthesize recent advances in AI applications for hemodialysis.
- To examine the potential, technical approaches, and practical effectiveness of AI in addressing hemodialysis management challenges.
- To support the transition towards an AI-driven, data-informed paradigm in hemodialysis care.
Main Methods:
- Narrative review of recent literature on AI in hemodialysis.
- Focus on five key domains: hemodynamic management, volume management, dialysis adequacy, vascular access, and renal anemia prediction.
- Discussion of emerging opportunities (wearable devices, multimodal data) and barriers to translation.
Main Results:
- AI applications are being developed across multiple domains of hemodialysis management.
- Wearable devices and multimodal data integration present emerging opportunities.
- A significant gap exists between retrospective AI performance and proven clinical outcome improvements.
Conclusions:
- AI has the potential to revolutionize hemodialysis management, enabling personalized, proactive care.
- Overcoming barriers to translation is crucial for realizing AI's full potential in improving hard clinical outcomes.
- The development of intelligent, closed-loop decision-support systems is facilitated by this review's insights.
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