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Core Viewpoint - The article discusses the implementation of the "Artificial Intelligence + Healthcare" initiative, aiming to enhance the quality of healthcare through AI technologies by 2027 and 2030, focusing on data collection, clinical applications, and the establishment of AI application bases [2][10]. Summary by Sections Overall Requirements - The initiative is guided by Xi Jinping's thoughts, emphasizing government guidance, multi-party participation, innovation-driven approaches, and safety [31]. - By 2027, high-quality data sets and trusted data spaces will be established, with widespread application of AI in clinical decision-making and patient services [32]. Key Application Areas - AI + Grassroots Applications: Focus on enhancing diagnostic capabilities and chronic disease management at the grassroots level [33]. - AI + Clinical Diagnosis: Promotion of intelligent diagnostic services in medical imaging and clinical decision support for specialized diseases [35]. - AI + Patient Services: Optimization of patient service processes, including intelligent appointment systems and follow-up care [36]. - AI + Traditional Chinese Medicine: Development of intelligent applications in TCM diagnosis and management [37]. - AI + Public Health: Strengthening disease monitoring and emergency management through AI [38]. - AI + Research and Education: Enhancing research efficiency and health education through intelligent applications [39]. - AI + Industry Governance: Promoting intelligent management in healthcare institutions and regulatory frameworks [40]. - AI + Health Industry: Encouraging the development of new intelligent health service models and innovative medical products [41]. Application Foundation - Infrastructure Development: Establishment of a national health information platform to connect all healthcare institutions [43]. - Data Supply Enhancement: Promotion of data sharing and optimization of data collection processes [43]. - AI Algorithm Optimization: Support for the development of core algorithms and AI models in healthcare [43]. - Pilot Base Construction: Creation of AI application pilot bases focusing on various healthcare sectors [44]. Safety and Regulation - Management and Review System: Implementation of a comprehensive governance mechanism for AI applications in healthcare [45]. - Innovative Regulatory Approaches: Strengthening monitoring and evaluation of AI applications to ensure safety and privacy [45]. - Data Security and Privacy Protection: Establishing systems for data management and personal information protection [46].