Ensuring safe and effective pharmacotherapy for children remains a central challenge in clinical pharmacology, yet rapid advances in AI have not translated into clinical practice. This Perspective highlights how AI‐enabled approaches can enhance model‐informed decision making for precision dosing. By integrating pharmacometrics with pediatric digital twins and AI agents, these frameworks can enable physiologically grounded, adaptive, and learning‐based dosing strategies. We outline a path from static prediction toward explainable, clinically actionable precision dosing in pediatric care.
Irie et al. (Fri,) studied this question.