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AI-powered insights in pediatric nephrology: current applications and future opportunities
Arwa Nada1, Yamen Ahmed2, Jieji Hu3
1Department of Pediatrics, Division of Pediatric Nephrology, Loma Linda University Children's Hospital (LLUCH), Loma Linda University (LLU), 11175 Campus St. A1120H, Loma Linda, CA, 92350, USA. anada@llu.edu.
Pediatric Nephrology (Berlin, Germany)
|September 16, 2025
Summary
Artificial intelligence (AI) is revolutionizing pediatric nephrology by enhancing diagnostics, treatment, and research. This technology offers personalized care for children with kidney diseases while addressing ethical considerations for responsible implementation.
Area of Science:
- Pediatric Nephrology
- Medical Artificial Intelligence
- Health Informatics
Background:
- Artificial intelligence (AI) is increasingly impacting healthcare, offering new avenues for improving patient care and operational efficiency.
- Pediatric nephrology faces challenges in diagnosis, treatment personalization, and managing administrative burdens.
Purpose of the Study:
- To explore the transformative potential of AI in pediatric nephrology.
- To highlight AI's role in enhancing diagnostic accuracy, therapeutic precision, and research.
- To address the ethical and practical challenges associated with AI implementation in this field.
Main Methods:
- Integration of diverse datasets (patient histories, genomics, imaging, clinical records) for AI analysis.
- Application of deep learning models for image interpretation (ultrasound, biopsies) and pathology.
- Development of AI-powered decision support systems, chatbots, and documentation tools.
Main Results:
- AI tools can detect kidney anomalies, predict acute kidney injury, and forecast disease progression.
- Deep learning improves ultrasound and biopsy assessments, optimizing medication and dialysis.
- AI enhances patient engagement and reduces physician burnout through chatbots and automated documentation.
Conclusions:
- AI offers significant potential to revolutionize pediatric nephrology, enabling individualized and proactive care.
- Addressing ethical concerns, ensuring data privacy, minimizing bias, and providing training are crucial for AI integration.
- AI can accelerate research by identifying biomarkers and therapeutic targets, fostering innovation and reducing disparities.
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