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

Area of Science:

• Pediatric Anesthesia
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly recognized for their potential in medical applications.
• Pediatric anesthesia presents unique challenges in airway management, intraoperative monitoring, and postoperative care.

Purpose of the Study:

• To review the current applications of AI and ML in pediatric anesthesia.
• To evaluate the performance of AI/ML models compared to traditional methods.
• To identify challenges and future directions for AI integration in pediatric anesthesia.

Main Methods:

• A systematic literature search was conducted across four databases up to 2024.
• Eleven studies examining AI methodologies in pediatric anesthesia settings were analyzed.
• The review focused on applications in airway management, intraoperative monitoring, and postoperative care.

Main Results:

• ML models demonstrated superior predictive performance over traditional approaches in areas like endotracheal tube sizing and placement, hypoxemia prediction, and pain assessment.
• Models achieved high accuracy (exceeding 90%) and significant reductions in placement errors (40-50%) in retrospective analyses.
• AI/ML applications showed consistent improvements in predictive capabilities within analyzed datasets.

Conclusions:

• AI and ML offer significant potential to enhance pediatric anesthesia practice, particularly in improving predictive accuracy and patient safety.
• Further research, including prospective multi-center validation and implementation studies, is crucial.
• Addressing challenges such as model generalizability, clinical workflow integration, regulatory compliance, and ethical considerations is essential for responsible AI adoption.