Front Nephrol. 2025 Feb 18;5:1548776. doi: 10.3389/fneph.2025.1548776. eCollection 2025.
ABSTRACT
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 access to optimize outcomes in pediatric AKI care.
PMID:40041609 | PMC:PMC11876175 | DOI:10.3389/fneph.2025.1548776
