A narrative review on artificial intelligence in advanced drug delivery systems: Nanomedicine, GEMs and smart robotics

Background Drug delivery systems are traditionally faced with the problems of bioavailability, controlled release, and targeted action, thus undermining the therapeutic action. This review explores how the integration of artificial intelligence (AI), including recent advances in explainable AI (XAI), is transforming pharmaceutical development and drug delivery technologies. Methodology Through a comprehensive review of recent literature, we summarize the roles of AI in enhancing active pharmaceutical ingredient (API) manufacturing, formulation design, and excipient selection, highlighting the growing importance of machine learning in identifying optimal excipient types and concentrations. The discussion involves improved active pharmaceutical ingredient manufacturing enabled by AI, improvement in formulation development (e.g., Formulation AI), and application of machine learning selection of excipients and their optimum concentration, with specific focus on the crucial role played by explainable AI (XAI) to ensure transparency and dependability in the drug development process. Scope The review emphasizes AI's multifaceted applications in drug delivery, such as designing and optimizing delivery vehicles, real-time monitoring with AI-based sensors, and AI-based repositories for scalable manufacturing. Novel delivery interventions are covered for diagnostic and personalized medicine uses. Liposomes, polymeric micelles, and dendrimers engineered for improved drug solubility and stability are of special interest. Such cutting-edge developments as caterpillar robots are also described. Conclusion The AI paradigm shows promise in changing drug development and delivery, yet its unrestrained infusion threatens to destroy human intervention, creativity, and ethics in research. Balancing AI with necessary human cognizance is crucial to secure the future of pharmaceutical invention