Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks

Digital transformation in pharmaceutical regulatory affairs is accelerating as global submissions grow in complexity and traditional document-based workflows reach their limits. Artificial intelligence (AI), particularly natural language processing (NLP), is increasingly being explored to support regulatory data management, document preparation, and decision support activities. This review examines AI adoption across pharmaceutical regulatory science, including initiatives from major regulatory agencies, AI-supported regulatory workflows, and emerging governance and interoperability frameworks. Current applications include document classification, data extraction, Common Technical Document (CTD) support, pharmacovigilance, and predictive analytics. Key implementation challenges, including explainability, traceability, validation, data quality, interoperability, cybersecurity, and Good Practice (GxP) compliance requirements, are critically discussed. The review further examines emerging regulatory data ecosystems and governance frameworks that may support the responsible integration of AI into regulatory processes. Collectively, these developments highlight the potential of AI to support more structured, interoperable, and efficient regulatory systems while maintaining regulatory oversight and accountability. Current evidence suggests that AI implementation has progressed from conceptual research toward early operational deployment. However, robust evidence demonstrating sustained improvements in regulatory performance and long-term operational impact remains limited.