Artificial Intelligence and Machine Learning: Enhancing the Future of Regulatory Affairs
Abstract
This review examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical regulatory affairs, with a focus on enhancing efficiency, accuracy, and decision-making in regulatory compliance, drug approvals, and post-market surveillance. Using peer-reviewed articles, regulatory guidelines, industry reports, and case studies, this article explores AI and ML tools such as Natural Language Processing (NLP), advanced analytics, machine learning algorithms, and automated document management. Key applications include risk assessment, regulatory compliance, and the streamlining of drug development processes. The review addresses challenges such as ethical considerations, data quality, transparency, and workforce training requirements that can hinder the implementation of AI and ML.
Future opportunities are identified, including the potential for regulatory agencies and pharmaceutical companies to leverage AI/ML for faster decision-making, reduced regulatory delays, and a more efficient regulatory landscape. However, successful integration will require ongoing workforce training, adaptive regulatory frameworks, and a focus on patient safety and public trust. In conclusion, while AI and ML hold promise for revolutionizing regulatory processes, addressing these challenges will be essential to ensure safe and ethical implementation.
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