The Impact of Artificial Intelligence Technologies on Nutritional Care in Patients with Chronic Kidney Disease: A Systematic Review.
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High-level infrastructural constraints emerged as serious challenges for implementation of multi-agent RAG systems in low-resource settings.
“High-level infrastructural constraints emerged as serious challenges of multi-agent RAG systems in low-resource settings for the implementation [46][47].”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [46]
Cites
KDOQI clinical practice guideline for nutrition in CKD: 2020 update
Ikizler, T. A., Burrowes, J. D., Byham-Gray, L. D., Campbell, K. L., Carrero, J.-J., Chan, W., Fouque, D., Friedman, A. N., Ghaddar, S., Goldstein-Fuchs, D. J., et al. (2020) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2020.05.006 · Cited by 1,960 · see citing works
Digital pathology and artificial intelligence in translational medicine and clinical practice
Baxi, V., Edwards, R., Montalto, M., & Saha, S. (2022) · Modern Pathology · doi:10.1038/s41379-021-00919-2 · Cited by 592 · see citing works
A review of the PubMed PICO tool: using evidence-based practice in health education
Brown, D. (2020) · Health Promotion Practice · doi:10.1177/1524839919893361 · Cited by 367 · see citing works
Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024) · Nutrients · doi:10.3390/nu16071073 · Cited by 253 · see citing works
Artificial intelligence: Essentials for nursing
McGrow, K. (2019) · Nursing · doi:10.1097/01.nurse.0000577716.57052.8d · Cited by 146 · see citing works
The Role of Artificial Intelligence in Nutrition Research: A Scoping Review
Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., & Oldewage-Theron, W. (2024) · Nutrients · doi:10.3390/nu16132066 · Cited by 114 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mugambiwa, T., & Ndlovu, B. (2026) · Journal of Applied Informatics and Computing · doi:10.30871/jaic.v10i1.11900 · Cited by 4 · see citing works
- Authors
- Palomares, S. M., Ferrara, G., Sguanci, M., Gazineo, D., Godino, L., Palmisano, A., Paderno, A., Vrenna, G., Faraglia, E., Petrelli, F., Cangelosi, G., Gravante, F., & Mancin, S.
- Affiliations
- University of Calabria (IT)
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- Humanitas University (IT)
- Università di Camerino (IT)
- Azienda Ospedaliera S.Giuseppe Moscati (IT)
- Corresponding author
- Cangelosi, Giovanni
- ORCID
- Venue
- Journal of Renal Nutrition
- Publisher
- Elsevier BV
- Published
- 2026-01
- Volume
- 36
- Issue
- 1
- Pages
- 13-23
- ISSN
- 1051-2276 (ISSN-L)
- OpenAlex
- W4411928071
- Cited by
- 17
- References
- 6
- Citations by year
- 2026: 11 · 2025: 6
- FWCI
- 1.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Renal Nutrition (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Unicam Scientific Publications (University of Camerino) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Health Sciences › Health Professions › Health Information Management › Dietetics, Nutrition, and Education
- Keywords
- artificial intelligence, chronic kidney disease, dietary management, personalized dietary recommendations, machine learning, clinical decision support systems, generative AI, hyperkalemia, dietary adherence, risk prediction, patient education, electronic health records, quality of life
- MeSH terms
- Artificial Intelligence, Humans, Quality of Life, Nutrition Therapy, Renal Insufficiency, Chronic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
