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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.

Clinical Pharmacology and Therapeutics
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can improve pediatric pharmacotherapy through precision dosing. Integrating AI with pharmacometrics and digital twins enables adaptive, learning-based dosing strategies for safer, more effective treatments in children.

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Area of Science:

  • Clinical Pharmacology
  • Artificial Intelligence in Medicine
  • Pediatric Pharmacotherapy

Background:

  • Ensuring safe and effective pharmacotherapy for children is a significant clinical challenge.
  • Advances in artificial intelligence (AI) have not yet been widely adopted in pediatric clinical practice.
  • Current dosing strategies often lack personalization for pediatric populations.

Purpose of the Study:

  • To highlight how AI-enabled approaches can enhance model-informed decision-making for precision dosing in children.
  • To propose frameworks integrating pharmacometrics, pediatric digital twins, and AI agents.
  • To outline a pathway toward explainable and clinically actionable precision dosing strategies.

Main Methods:

  • Integration of pharmacometrics with pediatric digital twins.
  • Utilization of AI agents for adaptive and learning-based dosing.
  • Development of physiologically grounded modeling frameworks.

Main Results:

  • AI-enabled approaches can enhance model-informed decision-making for precision dosing.
  • Proposed frameworks enable adaptive and learning-based dosing strategies.
  • A path is outlined from static prediction to dynamic, clinically actionable precision dosing.

Conclusions:

  • AI offers significant potential to optimize pharmacotherapy in pediatric care.
  • Integrating AI with pharmacometrics and digital twins can lead to personalized and effective dosing.
  • Future efforts should focus on developing explainable AI for clinical implementation in pediatric precision dosing.