生成式人工智能赋能卫生健康标准体系构建现状与挑战OA
The Current Situation and Challenges of Building a Standards System for High-quality Development of Health Care Empowered by Generative Artificial Intelligence
以大语言模型(LLMs)为代表的新一代生成式人工智能技术,深刻影响着卫生健康领域的高质量发展.生成式人工智能在临床辅助诊疗、疾病预防控制和健康管理等卫生健康领域的快速应用,对该领域的制度设计与治理工具提出了挑战.卫生健康领域标准作为保障医疗卫生质量、安全与基本公共卫生服务均等化的重要基础性制度,在医疗卫生技术快速迭代、服务模式持续变革和健康需求日益多元化的背景下,亟须直面生成式人工智能赋能卫生健康高质量发展之标准体系构建的现状和挑战.本文在系统梳理生成式人工智能在卫生健康领域应用研究进展的基础上,综述其在卫生健康标准体系构建中的赋能作用,从标准制定、实施、评估与动态修订等关键环节系统分析其潜在应用路径,并进一步探讨在技术可靠性、伦理治理、法律责任、数据质量与组织能力等方面面临的主要挑战.研究认为,在坚持以人为本和风险可控原则的前提下,稳妥推进生成式人工智能与卫生健康标准体系的融合,对于提升我国卫生健康治理现代化水平、促进健康公平具有重要意义.
Generative artificial intelligence(AI),especially the new generation of generative AI technologies represented by Large Language Models(LLMs),is profoundly transforming and reshaping the development path toward high-quality development in the field of health care.Generative AI technologies have rapidly expanded from technical-level applications in health care:such as clinical auxiliary diagnosis and treatment,disease prevention and control,and health management-to exert an impact on the institutional design and governance tools of this sector,thus posing new challenges.As an important foundational institutional guarantee for ensuring the quality and safety of medical and health services and the equity of basic public health services,the standard system in the health care field is confronted with an urgent demand for digital and intelligent transformation.Against the backdrop of the rapid iteration of medical and health technologies,the continuous evolution of service models,and the increasingly diversified health needs,it is imperative to directly address the current status and challenges in constructing a standard system empowered by generative AI to advance the high-quality development of health care.Building on a systematic review of recent research on the application of generative AI in health and wellness,this paper focuses on its enabling role in the development of health and wellness standards.It systematically examines potential application pathways across key stages:including standard formulation,implementation,evaluation,and dynamic updating:and further discusses major challenges related to technical reliability,ethical governance,legal liability,data quality,and organizational capacity.The study concludes that,guided by the principles of human-centered design and risk controllability,the deliberate integration of generative artificial intelligence into the health and wellness standards framework holds significant promise for advancing the modernization of health governance and promoting health equity in China.
陈开元;王洪川;葛袆;孙乃玲;陈澄;王天然;曾华堂;雷苏文;梁万年
100084 北京市,清华大学万科公共卫生与健康学院100084 北京市,清华大学公共管理学院100084 北京市,清华大学公共管理学院102206 北京市,中国疾病预防控制中心卫生标准处518000 广东省深圳市卫生健康发展研究和数据管理中心100084 北京市,清华大学万科公共卫生与健康学院518000 广东省深圳市卫生健康发展研究和数据管理中心||100084 北京市,清华大学生物医学工程学院102206 北京市,中国疾病预防控制中心卫生标准处100084 北京市,清华大学万科公共卫生与健康学院||518055 广东省深圳市,南方科技大学全科医学院
医药卫生
人工智能生成式人工智能大语言模型卫生健康标准标准体系数字健康
Artificial intelligenceGenerative artificial intelligenceLarge language modelsHealth standardsStandard systemDigital health
《中国全科医学》 2026 (22)
3073-3078,6
中国疾病预防控制中心资助项目(生成式人工智能赋能卫生健康的标准体系构建和政策策略研究项目)清华大学自主科研计划资助
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