FDA全面接入AI，监管走进深水区

Core Viewpoint - The FDA is implementing a comprehensive generative AI system across its organization by June 2025, marking a significant shift towards regulatory intelligence and efficiency in drug review processes [3][4][21]. Group 1: FDA's AI Implementation - The FDA's Director, Martin Makary, announced that all regulatory centers must fully integrate the generative AI system by June 30, 2025, to assist in various review tasks, significantly improving efficiency [3][4]. - This initiative is led by the newly appointed Chief AI Officer, Jeremy Walsh, who aims to create a unified, secure AI system embedded within the FDA's data platform, moving beyond simple AI tools to a more integrated operational model [4][9]. - The FDA's previous pilot projects demonstrated that AI could drastically reduce review times, with one expert noting that tasks that took three days could now be completed in minutes [3][8]. Group 2: Historical Context and Strategic Direction - The FDA's journey with AI began in 2021 with the "Digital Health Technologies Plan," which aimed to incorporate AI/ML into its regulatory modernization strategy [6][8]. - In January 2023, the FDA released the "Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device Action Plan," transitioning AI from an evaluation subject to an internal capability to enhance review efficiency [6][8]. Group 3: Global Comparison and Regulatory Landscape - The FDA is the first major regulatory body to set a clear timeline for a comprehensive AI rollout, supported by its long-term data governance and modernization efforts [12][18]. - Other global regulatory bodies, such as the EMA and Japan's PMDA, are still in exploratory phases, focusing on ethical considerations and small-scale trials, while China's NMPA has made significant progress in AI medical device approvals but is still in early stages of integrating AI into internal processes [16][19]. Group 4: Implications for the Industry - The FDA's transition signals three key implications for the industry: a potential restructuring of R&D timelines due to faster review processes, an increased emphasis on data quality for AI processing, and a more informed regulatory approach as regulators adopt AI tools themselves [18][19]. - Companies are encouraged to prepare structured and standardized submission materials to facilitate AI involvement in initial reviews, enhancing data consistency and quality [22].