Ai in Therapeutic Targeting: Redefining Drug Delivery through Smart Systems

Abstract

The fusion of Artificial Intelligence (AI) with modern drug delivery systems marks a pivotal shift in the way therapeutics are designed, administered, and monitored. Traditional drug delivery platforms have long struggled with issues like off-target effects, variable bioavailability, and poor patient adherence. Smart drug delivery systems aim to overcome these limitations by responding to internal or external physiological stimuli—offering precise, targeted, and often self-regulated release of medications. When integrated with AI, these systems gain further intelligence: enabling real-time decision-making, predicting release kinetics, optimizing formulations, and personalizing dosing strategies. This review explores the evolving landscape of AI-assisted smart drug delivery systems, highlighting how machine learning, deep learning, and predictive analytics are redefining the design and deployment of nanocarriers, wearable devices, and hybrid platforms. Special focus is given to AI’s role in material selection, pharmacogenomics, patient stratification, and theranostics. We also address critical challenges related to data privacy, regulatory ambiguity, algorithmic transparency, and ethical accountability. Moreover, emerging opportunities such as digital twins, closed-loop systems, and open-source AI platforms are discussed for their transformative potential. Together, AI and smart delivery platforms offer a promising vision of personalized, adaptive, and data-driven healthcare. As innovation continues to bridge computation with clinical application, the next generation of therapeutics may be as intelligent as they are effective—heralding a future where precision medicine is not just ideal, but inevitable.