Artificial Intelligence in Precision Pharmacotherapy: Clinical Applications, Opportunities, and Challenges

摘要

Abstract: Artificial intelligence is emerging as a key enabler of precision pharmacotherapy by improving individualized drug selection,therapeutic monitoring, and dose optimization. Machine learning models integrate clinical, molecular, pharmacokinetic, and longitudinal data to characterize interpatient variability and anticipate therapeutic response. Clinical applications include prediction of benefit and toxicity, pharmacogenomic interpretation, therapeutic drug monitoring, and adaptive dosing. These opportunities align with priorities to reduce adverse events, enhance treatment efficiency, and personalize therapeutic trajectories. However, challenges remain in validation,generalizability, interpretability, regulation, and workflow integration. Translational progress will depend on hybrid approaches that combine pharmacological knowledge with data-driven inference, interpretable decision support, and uncertainty-aware prediction. Future development may expand closed-loop exposure control, multi omics integration, virtual physiological models, and model informed trial design. Artificial intelligence has potential to reshape individualized therapy by making response more predictable, dosing more adaptive, and clinical reasoning more anticipatory.