关键词: 药品监管, 药品生命周期, 人工智能, 监管策略, 监管方法

Abstract:

With the widespread application of artificial intelligence （AI） technology in the pharmaceutical industry， global major drug regulatory agencies are facing AI application regulatory challenges. Based on reviewing the application of AI technology in the drug life cycle and the system of AI regulatory laws， regulations and guidelines of major regulatory agencies around the world on regulation of AI application， this study analyzes the specific strategies and methods adopted by these agencies in AI regulation.For instance， the U.S. FDA divides into 3 core modules about the drug life cycle AI governance， including human-led governance， accountability， and transparency； data quality， reliability and representativeness； model development， performance evaluation， monitoring and validation. The European EMA emphasizes the risks associated with AI applications in the drug life cycle， particularly in terms of data quality， model development， performance evaluation， and interpretability. Meanwhile， from a global perspective， WHO identifies the main risks of AI in drug development and supply， and divides AI regulation into six key areas， including documentation and transparency， risk management and AI system lifecycle approaches， intended use and clinical validation， data quality， privacy， and data protection， etc. Through comparative analysis， this study summarizes the commonalities and differences in AI regulation among global major drug regulatory agencies and proposes regulatory strategies and methods such as risk-based classification and grading， design-based regulatory strategies， labeling of AI-generated outputs， AI-specific validation processes， continuous regulation， and human accountability. The implementation of these strategies will help establish a more robust and trustworthy environment for AI applications in pharmaceutical， ensuring drug safety， efficacy， and quality control， while promoting innovation in the pharmaceutical industry.

Key words: Drug regulation； , Drug life cycle； , Artificial intelligence； , Regulatory strategies； , Regulatory methods