Objectives: Chronic kidney disease is a global health challenge, and effective, individualized nutritional management is crucial for slowing progression and improving quality of life. Artificial intelligence (AI) offers innovative tools to optimize and personalize nutritional care. This review explores AI applications in nutritional management, assessing their impact on clinical outcomes, quality of life, and care efficiency. Methods: A systematic review was conducted, reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Searches were performed on 5 databases, namely MEDLINE, Embase, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and integrated with gray literature sources between September and November 2024. The methodological quality assessment was conducted independently by 2 researchers using the Joanna Briggs Institute methodology. Results: Of 2,053 initial records, 7 studies met inclusion criteria. AI showed significant potential in personalizing dietary recommendations using machine learning, clinical decision support systems, and generative AI tools. These systems tailored nutritional advice based on patient-specific clinical data, reducing complications such as hyperkalemia and improving adherence. AI also facilitated early risk detection and proactive care by monitoring nutritional parameters and predicting complications. In addition, AI-powered platforms enhanced patient education through culturally relevant, intuitive dietary plans and multilingual materials, increasing engagement. AI also improved health care efficiency by automating tasks and integrating with electronic health records. Conclusions: AI technologies show promise in enhancing nutritional care for patients with chronic kidney disease. Evidence supports their role in improving care quality and dietary adherence. Further research is needed to validate these technologies in clinical practice and ensure integration into routine care pathways.
The Impact of Artificial Intelligence Technologies on Nutritional Care in Patients With Chronic Kidney Disease: A Systematic Review
Petrelli, Fabio;Cangelosi, Giovanni
;
2025-01-01
Abstract
Objectives: Chronic kidney disease is a global health challenge, and effective, individualized nutritional management is crucial for slowing progression and improving quality of life. Artificial intelligence (AI) offers innovative tools to optimize and personalize nutritional care. This review explores AI applications in nutritional management, assessing their impact on clinical outcomes, quality of life, and care efficiency. Methods: A systematic review was conducted, reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Searches were performed on 5 databases, namely MEDLINE, Embase, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and integrated with gray literature sources between September and November 2024. The methodological quality assessment was conducted independently by 2 researchers using the Joanna Briggs Institute methodology. Results: Of 2,053 initial records, 7 studies met inclusion criteria. AI showed significant potential in personalizing dietary recommendations using machine learning, clinical decision support systems, and generative AI tools. These systems tailored nutritional advice based on patient-specific clinical data, reducing complications such as hyperkalemia and improving adherence. AI also facilitated early risk detection and proactive care by monitoring nutritional parameters and predicting complications. In addition, AI-powered platforms enhanced patient education through culturally relevant, intuitive dietary plans and multilingual materials, increasing engagement. AI also improved health care efficiency by automating tasks and integrating with electronic health records. Conclusions: AI technologies show promise in enhancing nutritional care for patients with chronic kidney disease. Evidence supports their role in improving care quality and dietary adherence. Further research is needed to validate these technologies in clinical practice and ensure integration into routine care pathways.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
