Artificial Intelligence and Precision Pharmacotherapy in Pediatrics: A New Paradigm in Therapeutic Decision-Making

Artificial Intelligence (AI) and Precision Medicine represent foundational pillars for transforming pediatric healthcare, as children exhibit age-specific pharmacokinetic variations requiring highly personalized therapeutic approaches that make AI an indispensable tool for optimizing pharmacological safety and efficacy. This review analyzes current AI applications in pediatric precision pharmacotherapy, examining clinical opportunities and implementation challenges. AI demonstrates tangible clinical impact across multiple domains: pharmacogenomics with predictive models achieving R² = 0.95 for drug exposure; adverse drug reaction prediction with 81.5% sensitivity and 79.5% specificity; clinical decision support systems with 93.4% accuracy in pediatric epilepsy diagnosis. AI implementation has reduced prescription distribution errors by 75% and improved adverse drug reaction detection by 65%. However, significant gaps persist as only 0.38% of pediatric AI models reach clinical testing level, and 77% of studies show high risk of bias. AI transforms pediatric pharmacotherapy from empirical approaches to evidence-based predictive strategies, converting pediatric vulnerability into an innovation catalyst. The technology shifts understanding from correlation to causality, enabling personalized dosing and transforming pharmacovigilance into proactive safety mechanisms. Successful implementation requires overcoming current limitations including algorithmic bias, data quality issues, ethical considerations, and validation through rigorous clinical studies specifically designed for pediatric populations. Future development of sophisticated AI models promises enhanced precision, but real-world validation through interdisciplinary collaboration remains imperative for building robust pediatric AI ecosystems that opti-mize therapeutic outcomes for this vulnerable population.