Liu Zhao, Zheng Yang, Fang Jun, Yuan Lin
Institute of Executive Development, National Medical Products Administration, Beijing, 100073, China.
China Society for Drug Regulation, Beijing, 100050, China.
Ther Innov Regul Sci. 2026 Mar 5. doi: 10.1007/s43441-026-00946-8.
Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.
人工智能（AI）和大数据在药品监管中的应用日益广泛，并在全球范围内展现出巨大潜力。美国食品药品监督管理局（FDA）通过战略框架、监管指南和试点项目制定了相对全面的方法。欧洲药品管理局（EMA）通过大数据特别工作组、DARWIN EU和多年工作计划推动人工智能的采用，而日本、加拿大和其他国家也推进了相关举措并加强了国际合作。在中国，智能监管已被纳入“十四五”规划及后续战略，在建立国家监管数据平台、药品追溯系统和人工智能试点应用方面取得了进展。尽管如此，药品监管中的人工智能仍处于探索阶段，面临着模型可靠性和可解释性有限、数据标准和互操作性不足、监管空白以及伦理和公众信任等问题。未来的进展将取决于加强监管标准、提升数据治理、提高监管能力以及深化国际合作，以实现更科学、智能和高效的药品监管。
