Predict, diagnose, and treat chronic kidney disease with machine learning - a systematic literature review

Aug 5, 2026

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A systematic review assesses how artificial intelligence and machine learning methods are used to predict, diagnose, and manage therapy for chronic kidney disease. Evidence is compiled from English-language studies retrieved from PubMed and summarized using a rapid review approach with one database. Sixteen variables are extracted, and PRISMA principles guide duplicated main steps. Sixty-eight articles meet inclusion criteria, with performance metrics reported non-homogeneously and clinical validation often lacking.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Abstract

Objectives

Methods

Results

Conclusions

Introduction

Definition and burden of CKD

Etiology and risk factors

Motivation for ML in CKD

Role of AI and ML

Journal of Nephrology [https://doi.org/10.1007/s40620-023-01573-4](https://doi.org/10.1007/s40620-023-01573-4) Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review Francesco Sanmarchi1 · Claudio Fanconi2,3 · Davide Golinelli1 · Davide Gori1 · Tina Hernandez‑Boussard2 · Angelo Capodici1,2 Received: 6 August 2022 / Accepted: 1 January 2023 © The Author(s) 2023, corrected publication 2023 Abstract Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD) . We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a “rapid review”, since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. Results From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice. * Angelo Capodici [angelo.capodici@studio.unibo.it](angelo.capodici@studio.unibo.it) 1 Department of Biomedical and Neuromotor Science, Alma Mater Studiorum, University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy 2 Department of Medicine (Biomedical Informatics), Stanford University, School of Medicine, Stanford, CA, USA 3 Department of Electrical Engineering and Information Technology, ETH Zurich, Zurich, Switzerland 1 3 Graphical abstract Keywords Chronic kidney disease · Machine learning · Artificial intelligence · Systematic review Introduction Chronic Kidney Disease (CKD) is a state of progressive loss of kidney function ultimately resulting in the need for renal replacement therapy (dialysis or transplantation) [1] . It is defined as the presence of kidney damage or an estimated glomerular filtration rate less than 60 ml/min per 1.73 m2, persisting for 3 months or more [2]. CKD prevalence is growing worldwide, along with demographic and epidemiological transitions [3] . The implications of this disease are enormous for our society in terms of quality of life and the overall sustainability of national health systems. Worldwide, CKD accounted for 2,968,600 (1%) disability-adjusted life-years and 2,546,700 (1% to 3%) life-years lost in 2012 [4] . Therefore, it is ofthe utmost importance to assess how to promptly and adequately diagnose and treat patients with CKD. The causes of CKD vary globally. The most common primary diseases causing CKD and ultimately kidney failure are diabetes mellitus, hypertension, and primary glomerulonephritis, representing 70–90% of the total primary causes [1, 2, 4] . Although

FAQs

What is the main goal of this systematic literature review?

To assess how artificial intelligence, including machine learning techniques, has been deployed to predict, diagnose, and treat chronic kidney disease, improving diagnosis and patient management.

How were studies selected and what review framework was used?

English-language studies were retrieved from PubMed and analyzed as a rapid review. The review follows PRISMA and the main steps were performed in duplicate.

What key limitation did the review identify in existing machine learning studies?

Reported performance metrics were not homogeneous across articles, making direct comparison difficult, and clinical evaluation/validation was rare, with only 6 of 68 studies done in a clinical context.

Aug 5, 2026

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