Global Health & Medicine 2026;8(3):148-153.

Proactive adoption of generative artificial intelligence (AI) in the operations of Japan's Pharmaceuticals and Medical Devices Agency (PMDA): Current initiatives, governance, and future perspectives

Amakasu K, Kawana J, Kotera O, Numanyu T, Nakajima A, Ishikawa K, Kobayashi Y, Uyama Y

Abstract

The Pharmaceuticals and Medical Devices Agency (PMDA) continues to face increasing operational demands stemming from growing regulatory complexity, expanding data volumes, and evolving scientific and societal expectations. In this context, the appropriate adoption of generative artificial intelligence has emerged as a potential approach for enhancing operational efficiency while reinforcing scientific rigor and accountability. This article describes the current status of generative artificial intelligence utilization at PMDA, outlines its governance framework, and discusses future perspectives for its sustainable application based on institutional experience, internal policy development, and planned/ongoing proof-of-concept activities conducted within PMDA. We summarize a phased implementation strategy that combines commercially available generative artificial intelligence tools for administrative support with the exploration of large language models in secure internal environments for scientifically specialized tasks. Central to this approach is a governance framework that emphasizes human-in-the-loop decision-making, staged evaluation, information governance, and staff capacity building. We also present practical use cases across information collection, analysis and evaluation, and dissemination activities to illustrate how generative artificial intelligence may support regulatory work without replacing human judgment. In conclusion, PMDA's experience suggests that proactive yet cautious adoption of generative artificial intelligence, grounded in robust governance and organizational learning, can improve productivity and enhance scientific capacity within regulatory authorities while maintaining public trust and institutional accountability.

KEYWORDS: generative artificial intelligence, regulatory science, operational efficiency

DOI: 10.35772/ghm.2026.01046

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