Artificial Intelligence in the Management and Treatment of Pediatric Burns: A Narrative Review of Current Applications and Future Directions

Pediatric burn evaluation is often subjective; early estimates of depth and TBSA% drive triage, surgery, and follow-up. Emerging artificial intelligence (AI) tools, especially image-based methods, aim to standardize assessment and extend expertise via telemedicine. To narratively review AI applications relevant to pediatric burn care across diagnosis (depth/healing), TBSA% estimation, treatment planning/monitoring, telemedicine, and rehabilitation, and to summarize practical and ethical considerations for translation. Narrative review of PubMed/MEDLINE and Embase (January 2000-June 2025) with citation chaining. Pediatric and mixed-age studies applicable to pediatric care were included; conference abstracts and preprints were excluded unless subsequently published. Heterogeneity precluded quantitative synthesis; results are qualitatively summarized. Evidence is strongest for image-based assessment (clinical photographs, multispectral/optical modalities) supporting depth/healing classification and TBSA% segmentation, with several studies showing promising discrimination compared with unaided clinical assessment. Telemedicine workflows for remote triage and follow-up appear feasible, while data-driven decision support for timing of surgery and advanced dressing strategies remains investigational. Pediatric-specific datasets, external validation against accepted comparators, and reporting across skin tones and ages are still limited. AI has the potential to complement clinician judgment in pediatric burn care, most immediately for early image-based assessment and remote follow-up. Broader adoption will require pediatric-focused datasets, rigorous external validation, transparent governance for privacy and bias, and clear human-in-the-loop oversight.