Artificial intelligence in pediatric intensive care: unlocking integrated monitoring for autonomic nervous system dysregulation

Autonomic nervous system dysregulation (ANSD) in critical illness is often assessed through heart rate variability (HRV). This commentary discusses a pilot study that examines the correlations between pupillometry, delta skin temperature, and HRV. It highlights the utility of these non-invasive bedside techniques and explores the potential integration of artificial intelligence (AI) and machine learning to enhance clinical practice. Artificial intelligence (AI) in medicine refers to computational techniques that enable machines to analyze data, recognize patterns, and make predictions. Machine learning (ML) is a subset of AI focused on building models that learn from data to improve performance over time. AI platforms, leveraging big data analytics, can generate comprehensive risk scores by incorporating metrics such as HRV and pupillometry into real-time dashboards. These advancements could revolutionize pediatric critical care by delivering personalized, data-driven insights, enabling clinicians to intervene at the earliest stages using non-invasive monitoring tools like pupillometry and skin temperature measurements.