Authors: Shah A et al.
Journal: Cureus, Volume 18, Issue 1, Article e102535, 10.7759/cureus.102535
Summary
This structured narrative review examines current and emerging applications of artificial intelligence and machine learning in pediatric anesthesia across airway management, intraoperative monitoring, and postoperative care. The authors systematically screened the literature through 2024 and identified 11 studies evaluating AI-based tools in real-world pediatric anesthesia settings, most of which were retrospective and single-center in design.
Across multiple domains, AI models consistently demonstrated better predictive performance than traditional clinical formulas within the datasets studied. In airway management, machine-learning models improved accuracy in predicting endotracheal tube size and insertion depth, with several studies showing substantial reductions in placement error compared with age- or height-based formulas. Intraoperatively, AI models using high-frequency physiologic and ventilator data showed strong ability to anticipate hypoxemia before clinical deterioration occurred, offering a potential window for proactive intervention. In postoperative care, AI-derived pain assessment tools demonstrated high accuracy in identifying children who required rescue analgesia, particularly in populations unable to reliably self-report pain.
Despite these encouraging findings, the review emphasizes important limitations. Most studies relied on retrospective data from homogeneous populations, raising concerns about generalizability and algorithmic bias when applied to diverse pediatric cohorts. External and prospective validation was uncommon. The authors also highlight practical barriers to implementation, including workflow integration, regulatory oversight, ethical concerns, transparency of model decision-making, and the risk of widening health disparities if AI tools are adopted unevenly.
Overall, the review frames AI as a promising adjunct rather than a replacement for clinician judgment in pediatric anesthesia. The authors stress that future progress depends on prospective, multicenter validation, improved interpretability of models, and development of responsible implementation frameworks that integrate AI safely and equitably into clinical practice.
What You Should Know
Most AI applications in pediatric anesthesia currently show promise in retrospective analyses, but real-world impact depends on external validation, bias mitigation, and careful integration into clinical workflows rather than raw predictive accuracy alone.
Key Points
• Scope: structured narrative review of AI/ML applications in pediatric anesthesia
• Evidence base: 11 studies, mostly retrospective and single-center
• Strongest signals: ETT size/depth prediction, hypoxemia prediction, and pain assessment
• Main limitations: lack of prospective validation, population homogeneity, and implementation barriers
• Takeaway: AI tools may enhance safety and precision, but require cautious, clinician-guided adoption
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