Artificial Intelligence in Drug Regulatory Science: A Comprehensive Review

Background: Artificial intelligence (AI) is revolutionizing the pharmaceutical and healthcare sectors, particularly in regulatory affairs (RA). Technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) are increasingly applied to streamline regulatory processes, enhance decision-making, and improve operational efficiency. Understanding the integration of AI in drug regulatory science is essential for optimizing compliance, oversight, and innovation. Methods: A comprehensive literature review was conducted to examine AI applications across various stages of drug development and regulatory processes. Key AI techniques, including ML and DL, were analyzed to assess their role in risk assessment, regulatory decision-making, post- market surveillance, and human–machine collaboration. Comparative analyses of AI applications were also performed to identify patterns, challenges, and best practices.