Regulatory Insights into Artificial Intelligence in Drug Delivery and Medical Devices

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The pharmaceutical industry is grappling with challenges that impede the sustainability of drug development programs, primarily due to escalating research and development costs coupled with diminishing efficiency. This chapter explores the potential of leveraging artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), to bring about a transformative impact on the drug development process. ML, characterized by its capacity to learn from data with or without explicit programming, holds promise for addressing the complexities inherent in pharmaceutical research. DL, employing artificial neural networks (ANNs) as a multi-objective simultaneous optimization technique, has demonstrated efficacy in optimizing drug delivery systems. AI has the potential to transform drug discovery, clinical trials, drug delivery, and medical devices, emphasizing alignment with regulatory guidelines. However, challenges such as data quality and model complexity limit its transformative impact on medicine delivery and device development.<br><br>This chapter is structured into three parts, each addressing a distinct aspect of AI in the pharmaceutical landscape. The first part provides a foundational introduction to AI in the pharmaceutical industry, elucidating its role in overcoming inherent challenges. The second part delves into the diverse applications of AI-based tools and systems, encompassing drug discovery, various drug delivery systems, and the development of medical devices. Finally, the third part of the chapter sheds light on the regulatory challenges associated with AI-based drug delivery and medical device development, offering insights into the evolving regulatory landscape.