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MINI REVIEW article

Front. Nephrol.
Sec. Pediatric Nephrology
Volume 5 - 2025 | doi: 10.3389/fneph.2025.1548776

Artificial Intelligence and Pediatric Acute Kidney Injury: A Mini-Review and White Paper

Provisionally accepted
Jieji Hu Jieji Hu 1*Rupesh Raina Rupesh Raina 2
  • 1 Northeast Ohio Medical University, Rootstown Township, United States
  • 2 Akron Children's Hospital, Akron, Ohio, United States

The final, formatted version of the article will be published soon.

    Acute kidney injury (AKI) in pediatric and neonatal populations poses significant diagnostic and management challenges, with delayed detection contributing to long-term complications such as hypertension and chronic kidney disease. Recent advancements in artificial intelligence (AI) offer new avenues for early detection, risk stratification, and personalized care. This paper explores the application of AI models, including supervised and unsupervised machine learning, in predicting AKI, improving clinical decision-making, and identifying subphenotypes that respond differently to interventions. It discusses the integration of AI with existing risk scores and biomarkers to enhance predictive accuracy and its potential to revolutionize pediatric nephrology. However, barriers such as data quality, algorithmic bias, and the need for transparent and ethical implementation are critical considerations. Future directions emphasize incorporating biomarkers, expanding external validation, and ensuring equitable application to optimize outcomes in pediatric AKI care.

    Keywords: Pediatric Nephrology, artificial intelligence, Acute Kidney Injury, machine learning, Hematopoeietic stem cell transplantation, risk score

    Received: 20 Dec 2024; Accepted: 30 Jan 2025.

    Copyright: © 2025 Hu and Raina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jieji Hu, Northeast Ohio Medical University, Rootstown Township, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.