Harnessing Data Science and Artificial Intelligence to Advance Pediatric Intensive Care: From Multi-Institutional Databases to Predictive Modeling

The integration of data science and artificial intelligence into pediatric intensive care is rapidly reshaping how clinicians collect, interpret, and act on patient data. Yet the PICU remains an environment where large-scale, rigorous data-driven research has historically been constrained by small sample sizes, heterogeneous datasets, and the complexity of pediatric physiology.

Multi-institutional databases and collaborative data networks have begun to address these challenges, enabling researchers to draw meaningful conclusions from larger and more diverse cohorts. In parallel, novel data sources — including continuous vital sign streams, waveform analysis, and electronic health records — are opening new possibilities for real-time monitoring and clinical decision support. Meanwhile, predictive models and large language models (LLMs) are emerging as powerful tools for risk stratification, outcome prediction, and workflow optimization in critical pediatric care settings.

This Research Topic invites original research, systematic reviews, methods papers, and perspectives on the application of data science and AI across the PICU. We welcome contributions covering, but not limited to:

-Multi-institutional database development, linkage, and governance for pediatric critical care research

-Novel informatics tools including continuous monitoring, physiological waveform analysis, and real-time data streams

-Machine learning and deep learning models for PICU outcome prediction and risk stratification

-Large language model applications in clinical decision support and documentation in pediatric critical care

-Ethical, regulatory, and implementation considerations for AI-driven tools in the PICU

-Real-world effectiveness studies leveraging electronic health record data

This collection aims to bring together clinicians, data scientists, and informaticists working at the intersection of technology and pediatric critical care, accelerating the responsible translation of data-driven approaches into clinical practice.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Pediatric Intensive Care Unit (PICU), Artificial Intelligence (AI), Predictive Modeling, Multi-Institutional Databases, Clinical Decision Support Systems (CDSS), Large Language Models (LLMs), Continuous Physiological Monitoring, Risk Stratification, Elect

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.