ARTIFICIAL INTELLIGENCE IN DRUG DELIVERY: REAL-TIME MONITORING, PERSONALIZED THERAPY, AND ADAPTIVE RELEASE
Deeptee, Pratigya, Deep Chandra, *Rani Kumari
ABSTRACT
Artificial intelligence (AI) is transforming smart drug delivery systems (SDDS) by enabling real-time physiological monitoring, patient-specific formulation design, and dynamically adaptive drug release. Unlike earlier reviews that address AI in general healthcare, this review focuses specifically on AI's role across the pharmaceutical drug delivery continuum, from nanocarrier engineering to closed-loop therapeutic control. We examine how machine learning (ML), deep learning (DL), genetic algorithms (GAs), and the Internet of Medical Things (IoMT) together enable continuous health surveillance, early pathological detection, and timely intervention. The contribution of AI to formulation optimization, including nanoparticle engineering, 3D-printed dosage forms, and design-of-experiments-assisted predictive modelling, is discussed alongside adaptive release algorithms that respond to biological microenvironment signals. Particular attention is given to theranostic platforms and AI-guided nanocarrier systems such as ethosomes, niosomes, nanostructured lipid carriers (NLCs), and nanocrystals. Prevailing challenges, including data standardization, regulatory compliance, model generalizability, and clinical translation barriers, are critically assessed, and future directions such as AI-guided nanorobotics, generative AI for drug design, and next-generation IoMT architectures are outlined. This review underscores the need for sustained interdisciplinary collaboration between pharmaceutical scientists and AI developers to realize the translational potential of intelligent drug delivery.
Keywords: Artificial intelligence; smart drug delivery systems; machine learning; real-time monitoring; adaptive drug release; nanocarriers; Internet of Medical Things; theranostics; formulation optimization; clinical translation.
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