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Bioengineering
Volume 12
Issue 10
10.3390/bioengineering12101069
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases

by
Alessia Nicosia
1,
Nunzio Cancilla
1,
José David Martín Guerrero
2,3,
Ilenia Tinnirello
1 and
Andrea Cipollina
1,*
1
Department of Engineering, Università degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128 Palermo, Italy
2
Department of Electronic Engineering, Escola Tècnica Superior d’Enginyeria, Universitat de València (ETSE-UV), Despatx 3.2.26 Avgda. Universitat, s/n., 46100 València, Spain
3
Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), 46010 València, Spain
*
Author to whom correspondence should be addressed.
Bioengineering 2025, 12(10), 1069; https://doi.org/10.3390/bioengineering12101069
Submission received: 27 August 2025 / Revised: 26 September 2025 / Accepted: 28 September 2025 / Published: 1 October 2025
(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)
Versions Notes

Abstract

Artificial Intelligence (AI) is transforming the healthcare field, offering innovative tools for improving the prediction, detection, and management of diseases. In nephrology, AI holds the potential to improve the diagnosis and treatment of kidney diseases, as well as the optimization of renal replacement therapies. In this review, a comprehensive analysis of recent literature works on artificial intelligence applied to nephrology is presented. Two key research areas structure this review. The first section examines AI models used to support early prediction of acute and chronic kidney disease. The second section explores artificial intelligence applications for hemodialytic therapies in renal insufficiency. Most studies reported high accuracy (e.g., accuracy ≥ 90%) in early prediction of kidney diseases, while fewer addressed therapy optimization and complication prevention, typically reporting moderate-to-high performance (e.g., accuracy ≃ 85%). Filling this gap and developing more accessible AI solutions that address all stages of kidney disease would therefore be crucial to support physicians’ decision-making and improve patient care.
Keywords: nephrology AI; machine learning dialysis; kidney disease; hemodialysis; prediction; detection; renal replacement therapy optimization nephrology AI; machine learning dialysis; kidney disease; hemodialysis; prediction; detection; renal replacement therapy optimization

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MDPI and ACS Style

Nicosia, A.; Cancilla, N.; Martín Guerrero, J.D.; Tinnirello, I.; Cipollina, A. Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases. Bioengineering 2025, 12, 1069. https://doi.org/10.3390/bioengineering12101069

AMA Style

Nicosia A, Cancilla N, Martín Guerrero JD, Tinnirello I, Cipollina A. Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases. Bioengineering. 2025; 12(10):1069. https://doi.org/10.3390/bioengineering12101069

Chicago/Turabian Style

Nicosia, Alessia, Nunzio Cancilla, José David Martín Guerrero, Ilenia Tinnirello, and Andrea Cipollina. 2025. "Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases" Bioengineering 12, no. 10: 1069. https://doi.org/10.3390/bioengineering12101069

APA Style

Nicosia, A., Cancilla, N., Martín Guerrero, J. D., Tinnirello, I., & Cipollina, A. (2025). Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases. Bioengineering, 12(10), 1069. https://doi.org/10.3390/bioengineering12101069

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