Dermatology 2026

Background: Artificial intelligence (AI) is increasingly investigated across dermatology, but evidence derived from adult populations cannot be assumed to generalise to children. Pediatric-specific validation, equity, safeguarding, and clinical utility therefore require distinct evaluation.

Objective: To critically synthesise pediatric-specific and pediatric-relevant evidence arising from a focused narrative review of AI imaging, remote dermatology, digital severity assessment and monitoring, and cross-cutting validation gaps, and to assess maturity across a four-tier translational pathway: algorithmic performance, external validation, prospective clinical utility, and real-world patient benefit.

Methods: A structured narrative review searched PubMed/MEDLINE/PMC and IEEE Xplore, supplemented by citation cross-referencing. Evidence was appraised with particular attention to pediatric population specificity, external validation, prospective evaluation, skin-tone representation, clinically meaningful outcomes, and implementation readiness.

Results: Evidence maturity was markedly heterogeneous. Automated atopic dermatitis severity assessment had undergone prospective validation in a pediatric cohort with substantial representation of darker Fitzpatrick skin types, although replication across additional centres remains limited. Human-mediated store-and-forward pediatric teledermatology had a substantially larger and more mature evidence base than AI-assisted teledermatology, which remained largely exploratory. Across pediatric AI imaging and monitoring applications, evidence was frequently single-centre, internally validated, exploratory, or absent for several conditions, while external validation and clinically meaningful outcome data remained limited. No qualifying prospective pediatric-specific AI diagnostic-accuracy or clinical-utility study was identified in searches performed up to 7 September 2026. Overall, technical performance substantially outpaced evidence of prospective clinical utility and patient-level benefit.

Conclusion: AI demonstrates credible pediatric-specific capability in selected applications, but technical accuracy alone does not establish clinical readiness. Responsible integration will require multicentre external validation, prospective clinical evaluation, representative pediatric datasets, equity-focused reporting, clinically meaningful outcomes, and appropriate human oversight.

Keywords: Artificial intelligence; Pediatric dermatology; Clinical validation; Machine learning; Teledermatology.

Dr. Vladyslava Muratova is a dermatologist based in Ukraine and affiliated with the University Teaching Hospital (UTH). Her professional interests include pediatric dermatology, digital dermatology, artificial intelligence in healthcare, and clinical innovation. She is particularly interested in exploring how emerging technologies can support dermatological diagnosis, monitoring, and evidence-based patient care. Dr. Muratova also engages with international dermatology and health-technology initiatives.