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Policy·G Space·Evidence-backed gain·Published 2026-09-20

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

Abstract: 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-supp…

TRV-2026-1151Peer-reviewedPermanent record — cite & verify
Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks

Insurance policy issued by the Post Office of the Japanese Government-General of Korea (January 1936) by The Government-General of Korea, Empire of Japan.. Public domain

The quick read

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. The review further examines emerging regulatory data ecosystems and governance frameworks that may support the responsible integration of AI into regulatory processes.

Main points
  • 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.
Gain

Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks: However, robust evidence demonstrating sustained improvements in regulatory performance and long-term operational impact remains limited.

The rundown

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.

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