Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.

Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constraints and substantial increases in cognitive and physical workloads, making workload reduction an urgent priority. We propose a tripartite collaboration model in which medical staff, artificial intelligence agents, and robots redesign hemodialysis workflows at the task level. Artificial intelligence agents support non-physical work through three coordinated modules: "Eye" integrates and visualizes multimodal data from dialysis machines, electronic health records, laboratories, and home or wearable monitoring to highlight early signals of deterioration; "Brain" uses machine learning to predict complications such as intradialytic hypotension and to support optimization of dry-weight estimation, anemia and chronic kidney disease-mineral and bone disorder management, and prescription trade-offs through scenario simulation; and "Language", based on large language models, drafts structured session summaries and plain-language explanations anchored to verified data, with clinician review to mitigate hallucinations and omissions. Robots reduce physical workload through equipment preparation, transport, and environmental maintenance, and may extend to reproducible vascular access surveillance using robotic ultrasound and, in the longer term, assisted cannulation. Clinicians/medical staff remain accountable for goal setting, value-laden decisions, communication, and authorization of automated outputs and actions. We also summarize governance requirements-interoperability, human factors evaluation, privacy and cybersecurity, and staged deployment starting from low-risk, verifiable functions. By delegating routine cognitive and physical work while preserving human responsibility and relational care, the model may enable more proactive, patient-centered hemodialysis and support sustainable staffing and workload reduction.