AI+医健产业可持续创新论坛：可持续创新案例推荐榜揭晓，大咖热议AI重塑医疗健康未来

Core Viewpoint - The integration of AI technology into the healthcare industry is essential for addressing challenges such as global aging, chronic disease burdens, and uneven distribution of medical resources, positioning AI as a systemic solution for sustainable healthcare development [1][3][4]. Group 1: AI Empowerment in Healthcare - AI has transitioned from pilot scenarios to ecosystem restructuring, enhancing efficiency and accessibility in healthcare services [8][11]. - The forum highlighted the need for a new medical data infrastructure to achieve breakthroughs in efficiency, value, and equity [9][11]. - AI applications in healthcare are rapidly evolving, with significant advancements in areas such as pediatric AI pre-consultation, digital imaging in dentistry, and remote dermatology consultations [11][15]. Group 2: Sustainable Innovation in Healthcare - The "2025 Sustainable Innovation Case Recommendation List" was released, showcasing 37 cases from 36 well-known domestic and foreign companies, focusing on urgent industry needs and aligning with the Healthy China 2030 strategy [6][18]. - The list categorizes cases into "International Innovation Localization," "Local Innovation Globalization," and "ESG Innovation Practices," aiming to promote experience sharing and resource integration within the industry [6][18][35]. Group 3: Globalization and Localization Strategies - Multinational companies are deepening localization strategies, evolving from local production to local R&D innovation, while leveraging their technological and resource advantages to foster local innovation [35][41]. - Chinese pharmaceutical companies are increasingly exploring international markets, integrating into the global innovation ecosystem, and ensuring that local innovations benefit a global audience [35][41]. Group 4: ESG and AI in Healthcare - ESG has become a core issue for sustainable development, with a growing focus on integrating AI technology ethics into ESG evaluation dimensions [48]. - Companies are encouraged to disclose AI model training data sources and bias correction mechanisms, ensuring compliance and ethical standards in AI applications [48]. Group 5: Future Directions and Challenges - The healthcare industry faces challenges in high-quality data sample supply and the translation of clinical research into practical applications [11][13]. - The need for a robust AI infrastructure that supports effective business models and enhances traditional services is emphasized, with a focus on data-driven decision-making [50][51].