Challenges and Ethical Considerations in AI-Driven Drug Delivery: Implications for Clinical Translation (Preprint)

Abstract

<sec> <title>BACKGROUND</title> The integration of artificial intelligence (AI) into personalized medicine is revolutionizing drug delivery by transitioning from the traditional “one-size-fits-all” approach to patient-specific therapeutic strategies. </sec> <sec> <title>OBJECTIVE</title> This review aims to explore the transformative role of artificial intelligence (AI) in personalizing drug delivery systems by leveraging genomic, proteomic, and metabolic data. </sec> <sec> <title>METHODS</title> A comprehensive literature review was conducted using electronic databases such as PubMed, Scopus, and Web of Science to examine studies related to AI in pharmacogenomics, smart drug delivery, biosensing technologies, and drug repurposing. Inclusion and exclusion criteria were applied, and findings were thematically synthesized. </sec> <sec> <title>RESULTS</title> AI facilitates real-time data interpretation and personalized therapy through smart nanoformulations, biosensor-enabled monitoring, and deep learning-based pharmacogenomic modeling. Applications in oncology, diabetes, and neurodegenerative disorders show improved treatment outcomes. However, challenges include data security, regulatory constraints, and interpretability of AI models. </sec> <sec> <title>CONCLUSIONS</title> AI bridges genomics and pharmaceutics, driving precision medicine. Innovations like AI-driven 3D printing and federated learning promise a new era in personalized healthcare. </sec>

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