人工智能赋能卫生健康服务专家共识制订专家组 1梁万年 1曾华堂1
作者信息
摘要
Abstract
Artificial intelligence(AI)demonstrates significant advantages in accelerating improvements in efficiency,accessibility,and precision of health services,thereby providing critical support for the implementation of a health-prioritized development strategy.Artificial Intelligence Empowering Healthcare Services:Expert Consensus from the Mangrove Health Conference in 2025,compiled through expert consultations and in-depth discussions,systematically elaborated on the conceptual framework,objectives,challenges,and implementation pathways of AI-enabled health services.The consensus emphasizes that the core objective of integrating AI into health services lies in achieving systemic transformation across six dimensions,including service delivery models,management mechanisms,and driving forces.It identifies four major challenges currently confronting implementation:resource constraints arising from inadequate data quality and shortages of skilled professionals;sustainability dilemmas caused by the absence of appropriate payment and reimbursement mechanisms;governance challenges resulting from gaps in legal and ethical frameworks;and limitations in application effectiveness due to underdeveloped evaluation systems.In response to these challenges,the consensus proposes a comprehensive implementation framework,encompassing the establishment of a five-dimensional support system—organization,financing,workforce,technology,and the rule of law—the development of a scientific evaluation system,a focus on strengthening primary-level capacity,and the promotion of international collaboration.This consensus provides both a theoretical foundation and practical guidance for the standardized application and large-scale deployment of AI in the health sector,offering important reference value for advancing the digital transformation of Chinese health service system.关键词
人工智能/卫生服务/卫生人力/激励机制/评价体系/专家共识Key words
Artificial intelligence/Health services/Health workforce/Incentive mechanism/Evaluation system/Expert consensus分类
医药卫生
