To medical products administrations of all provinces, autonomous regions, and municipalities directly under the central government, and Xinjiang Production and Construction Corps, as well as all departments and affiliated institutions of the NMPA:

Since the implementation of the Drug Smart Regulation Action Plan, drug regulatory authorities at all levels have actively explored the use of information technology to enhance drug regulation capabilities, and have initially established a nationally-integrated drug smart-regulation system. Currently, the rapid development and iterative advancement of new-generation information technologies, such as AI, provide new tools and inject new impetus into smart regulation. To implement the Opinions of the State Council on In-depth Implementation of the "Artificial Intelligence +" Initiative and the Opinions of the General Office of the State Council on Comprehensively Deepening the Reform of Regulation of Drugs and Medical Devices to Promote the High-Quality Development of the Pharmaceutical Industry, seize the major strategic opportunity presented by the development of AI, promote the in-depth integration of AI with drug regulation, and accelerate the modernization of drug regulation, the following opinions are hereby formulated as follows.

I. General Requirements

Guided by Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era, we will thoroughly implement the spirit of the 20th National Congress of the Communist Party of China and the successive plenary sessions of the 20th Central Committee, and earnestly follow General Secretary Xi Jinping's important instructions on drug regulation. We will adhere to information technology-led modernization of drug regulation, stay problem-oriented and systematic in thinking, balance development and safety, leverage the Smart Regulatory Information Platform as the main hub, strengthen system collaboration and open sharing, take data elements as the driver and scenario applications as the traction. We will advance the innovative application of AI in the whole lifecycle of drug regulation, improving the level of "one-stop online services, one-network unified governance, and one-network collaboration" through automation, precision, collaboration, and intelligence. We will build a high-level, integrated national drug smart regulatory system to provide strong data-driven support for comprehensively deepening drug regulation reform.

By 2030, an integrated innovation system for AI + drug regulation will be preliminarily established. The operational management mechanism of "AI + Drug Regulation" will be essentially formed, the computing power support infrastructure will become more integrated and efficient, and high-quality datasets, vertical large models, and intelligent agents that meet regulatory intelligence needs will be developed. AI will be effectively applied in various scenarios such as review and approval, supervision and inspection, testing and surveillance, and government services. The efficiency of human-machine collaboration will be significantly enhanced, and digital intelligence-driven regulatory capabilities throughout the whole lifecycle will reach new heights. By 2035, a new pattern of smart drug safety governance featuring digital-driven, intelligent and agile, independent controllability and ecological collaboration will be basically formed.

II. Focus on Key Scenarios of Smart Regulation and Empower the Implementation of Regulatory Reforms through Digital-Intelligence

(I) Build a human-machine collaborative intelligent review and approval system. Promote the standardization and structuring of electronic submission of application dossiers, improve the review and approval knowledge base, and accelerate the R&D and application of large models and intelligent agents for the review of "drugs, medical devices, and cosmetics." This will efficiently empower scenarios such as intelligent product classification, task assignment, documents review, knowledge retrieval, problem identification, report generation, and certificate delivery, thereby significantly improving the quality and efficiency of review and approval. Focusing on high-frequency scenarios in the review and approval work of local regulatory authorities, and following the principle of NMPA guidance and the collaborative division of responsibilities among provincial medical products administrations (PMPAs), we will accelerate the implementation of intelligent applications in key scenarios, using pilot projects to drive broader adoption. These scenarios include the review of Class II medical devices, post-market changes for drugs, the filing of general cosmetics, and approval of manufacturing and distributing licenses. This will strengthen transition and sharing of achievements while avoiding low-level, redundant development. Further refine the working mechanism for AI-assisted review and approval work. With ensuring product safety and efficacy as the baseline and improving review quality and efficiency as the focus, we will establish and refine a human-machine collaboration mechanism featuring "data-intelligence empowerment, manual review, and full-process traceability," and accelerate the development of an efficient, safe, and manageable intelligent review and approval system.

(II) Enhancing intelligent regulatory capabilities across the entire chain. In the research and development stage, continue to advance the standardization of clinical trial data governance. Research and formulate technical guidelines for electronic records of clinical trials, computerized system validation, and other supporting regulations, improve the technical guidelines system, and leverage clinical trial big data to enhance regulatory effectiveness. In the production stage, we will continuously refine the digital intelligence regulatory mechanism for high-risk products such as vaccines, blood products, and special drugs. We will improve the regulatory model that combines on-site inspections with off-site surveillance, develop and deploy intelligent risk monitoring agents, and dynamically monitor quality and safety risks in the production process based on real-time analysis of enterprise production process data, such as surveillance video, images, and IoT sensor data. In the distribution and use stage, we will promote the digital and intelligent upgrading of the drug traceability system, further urge enterprises to fulfill primary traceability responsibilities, and enable platform enterprises to enhance their technical support capabilities and service levels. We will accelerate the serialization and coding of all marketed products to achieve full-process traceability across production, distribution, and use. Relying on the traceability collaboration platform, accelerate the filing of traceability coding rules for all products and build a multi-code relational mapping database linking drug traceability codes with product barcodes, medical insurance codes, and other codes. Strengthen information-based traceability regulation throughout the entire process for key products, and deepen trigger-based traceability regulation. Develop technical guidelines for typical applications of Unique Device Identification (UDI) for medical devices in the production, distribution, and use stages, and actively explore its application across the entire-chain regulation.

(III) Promoting the digital intelligence upgrade of the risk regulatory system. Promote the multi-source aggregation, intelligent analysis, graded dissemination, and traceable tracking of risk clues. Advance data-driven regulation, and refine the risk consultation mechanism featuring "monitoring and early warning — consultation and analysis — directive handling — tracking and retrospective review". Strengthen capabilities in risk perception, intelligent early warning, and coordinated response for key products, enterprises, and stages, thereby comprehensively enhancing the effectiveness of risk regulation. Further advance the Smart Drug Testing Initiative, promote the development of an integrated, digital and intelligent testing system, improve testing efficiency, accuracy, and the ability to identify risk signals, and encourage exploration of robotics technology in testing. Upgrade and refine the monitoring and evaluation system for "drugs, medical devices, and cosmetics," and promote the intelligent transformation of scenarios such as report submission, review and evaluation, intelligent analysis, risk warning, and cross-level collaboration, thereby enhancing the level of intelligent monitoring and evaluation. Establish an intelligent analysis and early warning system for complaints and reports. Coordinate the upgrading of the safety risk and public opinion monitoring system for the online sales of "drugs, medical devices, and cosmetics," and refine the working mechanism to achieve full-domain real-time monitoring, accurate assessment, scientific early warning, and effective response. Strengthen the intelligent analysis of traceability data, build traceability risk screening and early warning models, and improve the level of intelligent monitoring for distribution risks. Enhance the integration, governance, and analysis of big data for whole lifecycle regulation. Focus on high-risk products and key scenarios, develop intelligent risk regulation models for quality safety, distribution anomalies, and online sales monitoring, create intelligent risk monitoring agents, and develop dynamic risk profiles for key products, key enterprises, and key stages.

(IV) Advancing the intelligent and standardized approach to inspections and law enforcement. Deepen the Smart Inspection Initiative, integrate and upgrade the inspection system, and build an integrated, intelligent, comprehensive management platform for smart inspections. Based on the big data of product and credit archives for "drugs, medical devices, and cosmetics," conduct risk assessment and determine inspection targets, frequencies, and plans scientifically and rationally according to risk levels, reducing repetitive inspections and implementing precise inspections. Encourage provincial-level drug regulatory departments to build unified systems for supervision, inspection, and law enforcement case handling, and strengthen digital intelligence support for city and county-level regulatory personnel in the supervision, inspection, and enforcement related to "drugs, medical devices, and cosmetics." Deepen the application of AI to support real-time information queries on regulated entities, real-time data capture during regulatory processes, intelligent discovery of violation clues, and automatic generation of documents and reports. Accelerate the standardization of inspection and law enforcement workflows, and improve the effectiveness and consistency of on-site inspection and law enforcement. Strengthen mobile inspection and law enforcement capabilities, implement "QR code-based entry into enterprises", and realize "fingertip management and palm-top inspection".

(V) Enhancing collaborative regulatory effectiveness. Enhance cross-region, cross-level, and cross-department collaborative regulatory capabilities using digital intelligence technology, focusing on solving prominent issues such as inadequate collaboration mechanisms, inefficient business flows, lack of information sharing, and difficulties in closing the loop on issue resolution. Leveraging the Smart Regulatory Information Platform, we will build an efficient, intelligent, multi-stakeholder national integrated business collaboration system, improve the management mechanism for collaborative item lists, standardize business collaboration processes, rules, and interface standards, and focus on key areas and critical stages such as clinical trials, registration verification, cross-provincial entrusted manufacturing, and the supervision of products selected in centralized volume-based procurement. We will advance intelligent assignment, full traceability, and closed-loop management of cross-level and cross-region collaborative operations. Strengthen cross-departmental regulatory information sharing and business coordination to promote the coordinated development and governance of "three medical linkages" (medical care, medical insurance, and pharmaceuticals), effectively supporting joint inspections, case handling, administrative-criminal law enforcement coordination, and clue disposal, and enhance collaborative regulatory effectiveness.

(VI) Enhancing the intelligence level of government services. Implement the ongoing requirements for the "Efficiently Handling One Matter (integrated government services)" initiative and strengthen departmental collaboration and service integration. Accelerate the development of "AI + Government Services," improve the policy service knowledge base, and integrate data such as policies and regulations, service guidelines, frequently asked questions, online consultations, user feedback, and historical processing records. Refine policy requirements, policy tags, and push conditions, and optimize algorithm models. Provide services such as intelligent Q&A, intelligent guidance, intelligent form pre-filling, and intelligent assisted handling for enterprises and the public, advancing the intelligence, precision, and convenience of government services.

(VII) Promoting collaborative digital intelligence development between regulation and industry. Focusing on the requirements for intelligent regulation, encourage and guide the industry to accelerate its digital intelligence transformation and upgrade, enhancing the digital intelligence level across the entire process, including drug research and development, manufacturing, quality testing, and post-market surveillance and evaluation. Accelerate the development of guiding principles for the standardized application of AI in the pharmaceutical industry to meet the needs of emerging technologies in the industry. Advance the full digital intelligence transformation of manufacturing and testing processes for high-risk products such as blood products and traditional Chinese medicine injections. Develop supporting regulatory requirements and gradually expand this to other products, guiding the industry to improve the quality control capabilities across the entire process in accordance with regulatory norms.

III. Seizing the New Trends in AI Development and Strengthening the "AI + Drug Regulation" Foundational Support

(I) Promoting the development of high-quality drug regulation datasets. Adhere to the principle of "scenario-driven, urgent needs prioritized" and, focusing on the core business scenarios of the entire drug lifecycle regulation and the practical needs of AI applications, proceed with the phased and stepwise development of high-quality drug regulation datasets. Further improve the nationwide integrated drug regulation data resource system, with national and provincial-level data centers as hubs. We will improve the data aggregation and governance system based on product archives, enterprise credit records, legal and regulatory libraries, and typical case databases, improve the data aggregation and governance system. This will enhance the accuracy, consistency, and usability of the data, providing foundational support for the development of high-quality datasets. Focus on the training, fine-tuning, and practical application of vertical large models for drug regulation. Clearly define data formats, quality, and content requirements according to specific scenarios, and develop scientific and unified collection standards and labeling guidelines. Conduct multi-source data fusion governance, professional annotation, and knowledge extraction. Build both general and specialized knowledge bases for drug regulation, forming a layered, classified, dynamically updated, and fully traceable across the entire lifecycle. Under the strict condition of ensuring security and privacy, orderly promote the compliant and efficient application of knowledge bases and high-quality datasets in scenarios such as model training, knowledge inference, and decision support.

(II) Strengthening the AI application support system. Adhere to business-driven approach and comprehensively advance the training, deployment, and application of large models in the field of drug regulation. Leverage existing smart regulation infrastructure to build a large model application and algorithm management platform. Develop model application guidelines and security standards, promote the co-construction and sharing of common technical components, enhance model and algorithm management capabilities, and foster technological interoperability, resource sharing, and ecosystem collaboration. Promote the deep integration of AI with business information systems, accelerating the large-scale implementation of AI-assisted regulatory scenarios. Focusing on the entire drug lifecycle regulation, construct a multi-agent collaborative mechanism, improve system linkage and business collaboration frameworks, and drive the intelligent upgrade of drug regulation capabilities.

(III) Strengthening computing power infrastructure. The NMPA will coordinate the planning of a multi-level intelligent computing power resource coordination system, with national and provincial-level regulatory departments advancing the provision of smart computing resources as needed. Create a standardized and scalable intelligent computing power foundation to meet the smart application needs of various network domains, such as the Internet, government extranet, and government intranet. Enhance cross-domain collaboration and disaster recovery capabilities, gradually forming a "co-building, co-governance, and co-sharing" deployment pattern to improve computing power support capabilities, ensuring sustained and stable support for regulatory intelligence.

(IV) Fortifying the security protection system. Strictly implement the security responsibility system, upgrade the cybersecurity protection system, and improve mechanisms for cybersecurity situational awareness, information sharing, joint analysis, threat warning, and traceability and attribution. Utilize AI technologies to enhance proactive cybersecurity defense capabilities and build an intelligent, collaborative protection system. Establish and improve the data security management system, clarify core and important data catalogs, and enhance the technical framework for data security protection. Strengthen AI risk monitoring and evaluation, establish algorithm transparency requirements and model validation standards, and strengthen security capabilities for models, algorithms, data resources, infrastructure, and application systems. Enhance AI application risk assessments, response, prevent confidential and sensitive information from entering non-confidential models, and promote the compliant, transparent, and trustworthy use of AI applications.

(V) Improving the construction and operation management mechanism. Adhere to the supportive role of AI in the drug regulation field, clearly define the functional boundaries and responsibilities for large models and various intelligent support applications, and avoid unapproved deployments, fragmented construction, and overlapping efforts. Establish a dedicated mechanism to oversee the governance of AI applications in drug regulation, coordinating model construction access, safety reviews, and scenario compliance reviews and more. Develop a management system for "AI + Drug Regulation," clarifying responsibilities and work standards. Improve the model and algorithm filing management system, establish basic principles and technical standards, and conduct validation and evaluation of the effectiveness and reliability of models and their supporting applications. Strengthen the management of training data, fine-tuning data, and knowledge bases, ensuring the legality of their sources, accuracy of content, compliance with use, and traceability throughout the process. Explore the authorized operation of public drug regulation data, build dedicated public data areas, promote field-specific and scenario-based authorization, and enhance the development and utilization of public drug regulation data.

IV. Organizational Implementation

Drug regulatory authorities at all levels shall deeply understand the new trends in AI development and regard it as a key lever to support the comprehensive deepening of drug regulation reform and as a strong support for enhancing drug regulation capabilities. These authorities shall coordinate and align relevant plans, increase investments, and promote the application of AI in frontline regulation. The approach shall be "promoting construction through application and integrating development with application," ensuring that AI plays a practical role in regulation. There shall be a focus on demonstration and leadership, targeting the challenges and bottlenecks in regulatory business, and deepening the innovative applications of smart regulation to effectively empower business innovation. Strengthen regulatory science research to provide technological support for "AI + Drug Regulation," and promote the translation and application of relevant major scientific and technological projects. Increase training efforts to enhance the digital thinking, digital skills, and digital literacy of the regulatory workforce.

　　NMPA

　　March 11, 2026

　　(April 2, 2026)