Applications of artificial intelligence in chronic kidney disease: a systematic review

Authors

  • Jalila Andréa Sampaio Bittencourt Federal University of Maranhão image/svg+xml
  • Aline Santana Figueredo Federal University of Maranhão image/svg+xml
  • Naruna Aritana Costa Melo Federal University of Maranhão image/svg+xml
  • Cindy Lima Pereira Federal University of Maranhão image/svg+xml
  • Yuri Armin Crispim de Moraes Federal University of Maranhão image/svg+xml
  • Margareth Santos Costa Penha Federal University of Maranhão image/svg+xml
  • Darah de Lourdes Costa Lindoso Marques Federal University of Maranhão image/svg+xml
  • Evelyn Feitosa Rodrigues Federal University of Maranhão image/svg+xml
  • Arthur André Castro da Costa Faculdade Unibras Gama. Departamento de Medicina. Santa Inês, MA, Brasil
  • Allan Kardec Duailibe Barros Filho Federal University of Maranhão image/svg+xml

DOI:

https://doi.org/10.11606/s1518-8787.2026060007308

Keywords:

Artificial Intelligence, Machine Learning, Renal Insufficiency, Chronic, Systematic Review

Abstract

OBJECTIVE: To identify the applications of artificial intelligence in the early diagnosis, prediction and management of chronic kidney disease, highlighting the algorithms used, clinical outcomes, and impacts on nephrology practice. METHODS: In this systematic review, PubMed and Scopus databases were searched without year or language restrictions, including studies published between 2019 and 2025. Article selection followed PRISMA 2020 criteria, considering patients at risk for or diagnosed with chronic kidney disease, artificial intelligence tools as interventions, and outcomes related to early detection and disease management. Data was extracted on algorithm type, sample size, clinical outcomes, and main findings. RESULTS: Twenty-six studies were included, demonstrating the predominance of supervised learning and deep learning algorithms, generally showing high-performance metrics such as accuracy, sensitivity, and specificity in identifying chronic kidney disease and its stages. However, recurring weaknesses were observed, especially regarding the control of confounding factors, external validation of models, and standardization of datasets. Critical analysis of the studies shows that, despite the promising potential of computational models in the management of chronic kidney disease, methodological gaps still exist that limit their generalization and incorporation into clinical practice. CONCLUSION: This review demonstrates that artificial intelligence is a strategic tool for prevention, early diagnosis, and individualized management of chronic kidney disease. However, progress in this area depends on the development of more robust models, externally validated and integrated into real clinical contexts, contributing safely and effectively to renal  health care.

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Published

2026-09-02

Issue

Vol. 60 (2026)

Section

Review

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Copyright (c) 2026 Jalila Andréa Sampaio Bittencourt, Aline Santana Figueredo, Naruna Aritana Costa Melo, Cindy Lima Pereira, Yuri Armin Crispim de Moraes, Margareth Santos Costa Penha, Darah de Lourdes Costa Lindoso Marques, Evelyn Feitosa Rodrigues, Arthur André Castro da Costa, Allan Kardec Duailibe Barros Filho

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Bittencourt, J. A. S., Figueredo, A. S., Melo, N. A. C., Pereira, C. L., Moraes, Y. A. C. de, Penha, M. S. C., Marques, D. de L. C. L., Rodrigues, E. F., Costa, A. A. C. da, & Filho, A. K. D. B. (2026). Applications of artificial intelligence in chronic kidney disease: a systematic review. Revista De Saúde Pública, 60, e41. https://doi.org/10.11606/s1518-8787.2026060007308