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AI-Enabled Precision Dosing in Pediatrics: Enhancing Model-Informed Decision Making
Kei Irie1, Tomoyuki Mizuno1,2
1Division of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
None:
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.
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