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However, conventional management (relying heavily on clinician experience and intermittent monitoring) often fails to provide personalized, real\u2010time, and proactive care. Efficient, data\u2010driven 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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\u2010based care toward an AI\u2010driven, data\u2010informed paradigm, facilitating the development of intelligent, closed\u2010loop decision\u2010support systems that cover the entire dialysis journey.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/hdi.70110","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T02:49:32Z","timestamp":1783046972000},"page":"708-720","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Hemodialysis: Current Clinical Applications and Future Perspectives"],"prefix":"10.1111","volume":"30","author":[{"given":"Hui","family":"Ren","sequence":"first","affiliation":[{"name":"Division of Renal Medicine Peking University Shenzhen Hospital, Peking University  Shenzhen 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