Immersive Virtual Reality for Pediatric Procedural Pain and Anxiety: A Structured Narrative Review and Developmentally Informed Framework for Clinical Matching and AI-Supported Personalization.

Background/Objectives: Immersive virtual reality (VR) is increasingly used during pediatric procedures, but intervention effects vary across children, procedures, devices, comparators, and outcomes. This review synthesized recent evidence and developed a proposed developmentally informed child-procedure-intervention framework for clinical matching. Methods: A structured narrative review synthesized 96 publications identified through PubMed, Scopus, Web of Science Core Collection, the Cochrane Library, and CINAHL through 31 July 2026. Explicit searching, eligibility criteria, screening, and structured evidence characterization were used to improve transparency; however, this was not a systematic review, and no formal risk-of-bias or certainty-of-evidence assessment was performed. Evidence was interpreted according to publication type and organized by clinical context, developmental modifiers, intervention characteristics, implementation, biofeedback, and future AI-supported decision support. Results: The most recurrent favorable findings concerned venipuncture, intravenous access, vaccination, and related needle procedures, particularly against usual care, whereas comparisons with active distraction and evidence from oncology, burns, dentistry, orthopedics, surgery, and emergency care were more mixed. The narrative synthesis identified developmental readiness, baseline anxiety, previous procedural experience, sensory tolerance, content, interactivity, immersion, timing, comparator, concurrent analgesia, and patient preference as plausible moderators, although their predictive value remains insufficiently validated. The author-generated framework links nursing assessment, purpose-based VR selection, monitoring, stopping criteria, and clinically approved adjustment. Conclusions: Pediatric procedural VR should not be treated as a uniform intervention. The proposed framework is evidence-informed and hypothesis-generating, not a validated clinical decision model. Biofeedback is supported by preliminary pediatric evidence, whereas AI-supported adaptation remains a future research and translational opportunity requiring prospective validation, governance, and continuous clinical oversight.