Sarcouncil Journal of Medical Series

Abstract: The increasing complexity and volume of electronic Common Technical Document (eCTD) submissions have amplified the demand for efficient and error-free quality control mechanisms in regulatory affairs. Traditional manual validation methods are time-consuming and prone to inconsistencies, posing risks to submission success and compliance. This paper explores the integration of artificial intelligence (AI) technologies, specifically machine learning (ML), natural language processing (NLP), and deep learning, into the quality control processes of eCTD compilation. It examines the AI methodologies applied, their implementation within real-world regulatory workflows, and the tangible benefits they offer in terms of speed, accuracy, scalability, and compliance. The discussion extends to the challenges faced in adopting AI solutions, including regulatory ambiguity, data privacy issues, and technical integration barriers. Finally, the paper highlights future directions, emphasizing innovations such as generative AI, predictive analytics, and blockchain integration that are expected to shape the next generation of AI-assisted regulatory submissions. The findings suggest that AI-based quality control has the potential to redefine the regulatory landscape by enabling smarter, faster, and more reliable eCTD submissions.