Research Journal of Science and Technology
  • Year: 2025
  • Volume: 17
  • Issue: 4

Al-Driven Regulatory Affairs: Current Applications and Future Directions in Drug Development

  • Author:
  • Tej Bhadresh Patel1,*, Anuradha Prajapati1, Sachin Narkhede1, Shailesh Luhar1, Mihir Thakor1
  • Total Page Count: 8
  • Published Online: May 2, 2026
  • Page Number: 305 to 312

1Smt. B.N.B Swaminarayan Pharmacy College, Salvav - Vapi, Gujarat, 396191

*Corresponding Author E-mail: tejpatel037@gmail.com

Online Published on 02 May, 2026.

Abstract

Artificial intelligence (AI) has revolutionized healthcare, enabling advancements in diagnostics and drug development. However, its application in drug regulation is still developing, with varying levels of adoption across global regulatory agencies. This review explores the current landscape of AI in drug regulation, focusing on its implementation and impact within these organizations. Findings indicate that many agencies are actively adopting AI strategies to enhance data-driven decision-making and optimize regulatory processes. AI is increasingly utilized for safety monitoring, workflow improvements, and, to a lesser extent, exploratory research in regulatory science. These efforts are expanding AI’s role in streamlining medicine regulation, marking a significant shift toward more efficient and evidence-based regulatory systems. Artificial intelligence (AI) is transforming regulatory affairs in drug development by streamlining processes, enhancing decision-making, and ensuring compliance with evolving global standards. Current applications include automated data analysis for regulatory submissions, real-time monitoring of compliance requirements, and predictive modeling to anticipate regulatory challenges. AI tools are being utilized to optimize clinical trial designs, improve pharmacovigilance through adverse event detection, and accelerate document preparation for regulatory approvals. These advancements reduce time-to-market and enhance efficiency while maintaining high standards of safety and efficacy. Looking forward, AI is poised to revolutionize regulatory affairs through advanced natural language processing for automated dossier generation, integration with real-world evidence for adaptive regulatory pathways, and personalized medicine approaches tailored to specific patient populations. However, challenges such as data privacy, ethical considerations, and the need for harmonized global regulations must be addressed to fully realize AI’s potential.

Keywords

Artificial intelligence (AI), Machine learning (ML), Regulatory intelligence, Document automation, Natural language processing (NLP)