# Applications of Artificial Intelligence in Pediatric Anesthesia: A Structured Narrative Review

## TL;DR

This paper reviews how AI and machine learning are being used in pediatric anesthesia to improve care and outcomes.

## Contribution

The paper systematically analyzes recent AI applications in pediatric anesthesia and highlights their potential and limitations.

## Key findings

- Machine learning models showed better performance than traditional methods in predicting hypoxemia and assessing pain.
- AI models achieved over 90% accuracy in some applications and reduced placement errors by 40-50%.
- Most studies were single-center and retrospective, indicating a need for broader validation.

## Abstract

Artificial intelligence (AI) and machine learning (ML) are emerging as valuable tools in pediatric anesthesia practice. This narrative review examines applications across airway management, intraoperative monitoring, and postoperative care. Following a systematic literature search of four databases through 2024, 11 studies were analyzed examining AI methodologies in pediatric anesthesia settings. The review found that ML models consistently demonstrated improved predictive performance within studied datasets compared with traditional clinical approaches, particularly in endotracheal tube sizing and placement, hypoxemia prediction, and pain assessment. Models achieved high predictive accuracy rates and discrimination metrics across diverse clinical applications, with some demonstrating placement error reductions of 40-50% in retrospective analyses and achieving prediction accuracy exceeding 90%. Despite promising results, most studies were retrospective single-center analyses, highlighting the need for prospective multi-center validation and implementation research. Key challenges include ensuring model generalizability across diverse populations, integrating AI into clinical workflows, addressing regulatory requirements, and maintaining transparency in decision-making processes. Future work should focus on external validation, improving model interpretability, and developing frameworks for responsible integration into pediatric anesthesia practice to maximize patient safety benefits while addressing ethical considerations.