Abstract

The integration of artificial intelligence (AI) into pharmacokinetic modeling represents a significant advancement in the field of precision medicine. AI techniques, including machine learning and deep learning, enable the analysis of complex datasets to predict drug behavior more accurately across diverse patient populations. This approach holds promise for optimizing drug dosing regimens, enhancing therapeutic outcomes, and minimizing adverse effects. However, challenges such as algorithm validation and ethical considerations must be addressed to realize AI's full potential in transforming pharmacokinetic modeling. This review explores the current landscape, challenges, and future directions of AI integration in pharmacokinetic modeling for precision medicine