J Korean Neurosurg Soc. 2026 Apr 9. doi: 10.3340/jkns.2026.0033. Online ahead of print.
ABSTRACT
Anesthesia for pediatric neurosurgery represents a highly complex and challenging field, characterized by age-dependent physiological variability, heterogeneous patient populations, and the critical need to protect the developing central nervous system. Conventional clinical approaches often rely on adult-derived data, which may inadequately reflect the distinct neurophysiological and hemodynamic characteristics of neonates, infants and children. Recent advances in artificial intelligence (AI) have enabled the integration of multimodal perioperative data, including physiologic signals and neurophysiologic monitoring, with the aim of supporting clinical decision-making in complex surgical settings. This review summarizes the current landscape of AI applications relevant to pediatric anesthesia, with particular attention to preoperative risk assessment, airway management, and real-time prediction of intraoperative adverse events such as hypoxemia and hemodynamic instability. Although AI-based approaches have demonstrated encouraging results in adult populations, their application to pediatric neuroanesthesia remains limited. The integration of AI into this field faces several distinct challenges, including the scarcity of high-quality datasets for rare neurosurgical conditions, substantial heterogeneity across developmental stages, and difficulties in aligning model outputs with clinically interpretable physiologic mechanisms. Addressing these limitations will require the development of explainable, physiology-informed AI frameworks and disease-specific models tailored to conditions such as moyamoya disease or complex craniofacial reconstruction. Ultimately, AI should be positioned as an adjunctive decision-support tool that complements, rather than replaces, anesthesiologists’ expertise. Through multidisciplinary collaboration and human-centered implementation, AI may contribute to improved perioperative safety and long-term neurodevelopmental outcomes in vulnerable pediatric neurosurgical patients.
PMID:41952534 | DOI:10.3340/jkns.2026.0033
