生成式人工智能赋能卫生健康标准体系构建现状与挑战

doi:10.12114/j.issn.1007-9572.2026.0010

中国全科医学 ›› 2026, Vol. 29 ›› Issue (22): 3073-3078.DOI: 10.12114/j.issn.1007-9572.2026.0010

生成式人工智能赋能卫生健康标准体系构建现状与挑战

陈开元¹, 王洪川², 葛袆², 孙乃玲³, 陈澄⁴, 王天然¹, 曾华堂⁴^,⁵, 雷苏文³^,^*(), 梁万年¹^,⁶^,^*()

1．100084　北京市，清华大学万科公共卫生与健康学院
2．100084　北京市，清华大学公共管理学院
3．102206　北京市，中国疾病预防控制中心卫生标准处
4．518000　广东省深圳市卫生健康发展研究和数据管理中心
5．100084　北京市，清华大学生物医学工程学院
6．518055　广东省深圳市，南方科技大学全科医学院

收稿日期:2026-01-14 修回日期:2026-04-08 出版日期:2026-08-05 发布日期:2026-07-08
通讯作者: 雷苏文, 梁万年
陈开元、王洪川为共同第一作者

作者贡献：
陈开元、王洪川提出主要研究目标，负责研究的构思与设计，研究的实施，撰写论文；葛袆、孙乃玲、陈澄、王天然、曾华堂进行论文的修订；雷苏文、梁万年负责文章的质量控制与审查，对文章整体负责，监督管理。
基金资助:
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

The Current Situation and Challenges of Building a Standards System for High-quality Development of Health Care Empowered by Generative Artificial Intelligence

CHEN Kaiyuan¹, WANG Hongchuan², GE Yi², SUN Nailing³, CHEN Cheng⁴, WANG Tianran¹, ZENG Huatang⁴^,⁵, LEI Suwen³^,^*(), LIANG Wannian¹^,⁶^,^*()

1. Vanke School of Public Health, Tsinghua University, Beijing 100084, China
2. School of Public Policy and Management, Tsinghua University, Beijing 100084, China
3. Division of Health Standards, Chinese Center for Disease Control and Prevention, Beijing 102206, China
4. Shenzhen Center for Health Development and Data Management, Shenzhen 518000, China
5. School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
6. College of General Practice, Southern University of Science and Technology, Shenzhen 518055, China

Received:2026-01-14 Revised:2026-04-08 Published:2026-08-05 Online:2026-07-08
Contact: LEI Suwen, LIANG Wannian
About author:
CHEN Kaiyuan and WANG Hongchuan are co-first authors

摘要/Abstract

摘要： 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。

关键词: 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

Abstract:

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.

Key words: Artificial intelligence, Generative artificial intelligence, Large language models, Health standards, Standard system, Digital health

中图分类号:

R 197

陈开元,王洪川,葛袆,等. 生成式人工智能赋能卫生健康标准体系构建现状与挑战[J]. 中国全科医学, 2026, 29(22): 3073-3078. DOI: 10.12114/j.issn.1007-9572.2026.0010.
CHEN Kaiyuan,WANG Hongchuan,GE Yi, et al. The Current Situation and Challenges of Building a Standards System for High-quality Development of Health Care Empowered by Generative Artificial Intelligence[J]. Chinese General Practice, 2026, 29(22): 3073-3078. DOI: 10.12114/j.issn.1007-9572.2026.0010.

参考文献 42

[1]	中华人民共和国国家卫生健康委员会. 国家卫生健康委关于印发十四五"卫生健康标准化工作规划的通知[A/OL]. (2022-01-27)[2026-01-08].
[2]	贺洪峰, 田倩男, 周奇, 等. 国内外卫生健康标准管理体系及制订现状的循证评价[J]. 协和医学杂志, 2024, 15(1): 202-210. DOI: 10.12290/xhyxzz.2023-0449.
[3]	吕兰婷, 张子墨. 我国医疗卫生标准管理的现状研究[J]. 中国卫生事业管理, 2019, 36(4): 258-260.
[4]	American National Standards Institute. American National Standards (ANS) introduction[EB/OL]. (2023-11-20)[2026-02-07].
[5]	Mandl K D, Gottlieb D, Mandel J C. Integration of AI in healthcare requires an interoperable digital data ecosystem[J]. Nat Med, 2024, 30(3): 631-634. DOI: 10.1038/s41591-023-02783-w.
[6]	李俊, 王强, 黄超, 等. 对我国医疗卫生标准工作的思考[J]. 中国卫生标准管理, 2020, 11(7): 4-7. DOI: 10.3969/j.issn.1674-9316.2020.07.002.
[7]	Zheng X, Curtis J P, Hu S, et al. Coronary catheterization and percutaneous coronary intervention in China: 10-year results from the China PEACE-retrospective CathPCI study[J]. JAMA Intern Med, 2016, 176(4): 512. DOI: 10.1001/jamainternmed.2016.0166.
[8]	Li J, Li X, Wang Q, et al. ST-segment elevation myocardial infarction in China from 2001 to 2011 (the China PEACE-Retrospective Acute Myocardial Infarction Study): a retrospective analysis of hospital data[J]. Lancet, 2015, 385(9966): 441-451. DOI: 10.1016/S0140-6736(14)60921-1.
[9]	Zeng X Y, Wang L J, Yin P, et al. Subnational analysis of healthcare access and quality in China during 1990-2015[J]. Chin Sci Bull, 2018, 63(25): 2631-2640. DOI: 10.1360/n972017-01159.
[10]	李希, 杨阳, 苏明明, 等. 我国学习型卫生健康体系构建研究[J]. 中国工程科学, 2025, 27(6): 106-115. DOI: 10.15302/J-SSCAE-2025.06.023.
[11]	刘苏苏, 茅宁莹. 江苏省卫生地方标准工作现状分析[J]. 中国卫生质量管理, 2021, 28(7): 22-24, 32. DOI: 10.13912/j.cnki.chqm.2021.28.7.06.
[12]	庞亚男, 郑彬. 我国卫生标准与世界卫生组织指南制定与管理的对比[J]. 中国卫生标准管理, 2018, 9(9): 5-10. DOI: 10.3969/j.issn.1674-9316.2018.09.003.
[13]	Banh L, Strobel G. Generative artificial intelligence[J]. Electron Mark, 2023, 33(1): 63. DOI: 10.1007/s12525-023-00680-1.
[14]	赵俊贤, 杨楠, 刘辉, 等. 我国卫生健康标准现状与发展[J]. 协和医学杂志, 2023, 14(6): 1325-1329. DOI: 10.12290/xhyxzz.2023-0347.
[15]	Teo Z L, Thirunavukarasu A J, Elangovan K, et al. Generative artificial intelligence in medicine[J]. Nat Med, 2025, 31(10): 3270-3282. DOI: 10.1038/s41591-025-03983-2.
[16]	Thirunavukarasu A J, Ting D S J, Elangovan K, et al. Large language models in medicine[J]. Nat Med, 2023, 29(8): 1930-1940. DOI: 10.1038/s41591-023-02448-8.
[17]	Lu C, Lu C, Lange R T, et al. The AI scientist: towards fully automated open-ended scientific discovery[PP/OL]. V3. arXiv (2024-09-01).
[18]	Thirunavukarasu A J. Large language models will not replace healthcare professionals: curbing popular fears and hype[J]. J R Soc Med, 2023, 116(5): 181-182. DOI: 10.1177/01410768231173123.
[19]	Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3[J]. Nature, 2024, 630(8016): 493-500. DOI: 10.1038/s41586-024-07487-w.
[20]	Deepseek-Ai, Guo D Y, Yang D J, et al. Deepseek-r1: Incentivizing Reasoning Capability In Llms Via Reinforcement learning[J]. arXiv preprint arXiv: 2501.12948, 2025.
[21]	Bengesi S, El-Sayed H, Sarker M K, et al. Advancements in generative AI: a comprehensive review of GANs, GPT, autoencoders, diffusion model, and transformers[J]. IEEE Access, 2024, 12: 69812-69837. DOI: 10.1109/ACCESS.2024.3397775.
[22]	Sumner J, Wang Y C, Tan S Y, et al. Perspectives and experiences with large language models in health care: survey study[J]. J Med Internet Res, 2025, 27: e67383. DOI: 10.2196/67383.
[23]	Abd-Alrazaq A, Alsaad R, Alhuwail D, et al. Large language models in medical education: opportunities, challenges, and future directions[J]. JMIR Med Educ, 2023, 9: e48291. DOI: 10.2196/48291.
[24]	Du H R, Zhao J N, Zhao Y, et al. Advancing real-time pandemic forecasting using large language models: a COVID-19 case study[PP/OL]. V1. arXiv(2024-04-10).
[25]	Yang R, Tan T F, Lu W, et al. Large language models in health care: development, applications, and challenges[J]. Health Care Sci, 2023, 2(4): 255-263. DOI: 10.1002/hcs2.61.
[26]	De Angelis L, Baglivo F, Arzilli G, et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health[J]. Front Public Heal, 2023, 11: 1166120. DOI: 10.3389/fpubh.2023.1166120.
[27]	Jo E, Epstein D A, Jung H, et al. Understanding the benefits and challenges of deploying conversational AI leveraging large language models for public health intervention[C]//Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, 2023: 1-16. DOI: 10.1145/3544548.3581503.
[28]	Xiong G Z, Jin Q, Lu Z Y, et al. Benchmarking retrieval-augmented generation for medicine[C]//Findings of the Association for Computational Linguistics ACL 2024. Bangkok, Thailand and virtual meeting, 2024: 6233-6251. DOI: 10.18653/v1/2024.findings-acl.372.
[29]	Nori H, King N, Mckinney S M, et al. Capabilities of GPT-4 on medical challenge problems[PP/OL]. V2. arXiv(2023-04-12).
[30]	Vrdoljak J, Boban Z, Vilović M, et al. A review of large language models in medical education, clinical decision support, and healthcare administration[J]. Healthcare, 2025, 13(6): 603. DOI: 10.3390/healthcare13060603.
[31]	贺洪峰, 田倩男, 周奇, 等. 国内外卫生健康标准管理体系及制订现状的循证评价[J]. 协和医学杂志, 2024, 15(1): 202-210.
[32]	郑鹰, 张欣亮, 张雪飞. 生成式人工智能国内外标准化发展状况、面临挑战及对策研究[J]. 标准科学, 2025(12): 35-40.
[33]	Nidhya R, Pavithra D, Manish K, et al. Generative Artificial Intelligence: Concepts and Applications[M]. Massachusetts: Scrivener Publishing LLC: 2025: 9-17.
[34]	李俊, 王强, 黄修森, 等. 对医疗卫生标准体系升级改造项目中标准制修订工作的思考[J]. 中国卫生标准管理, 2023, 14(3): 1-5.
[35]	苏仁凤, 刘辉, 史乾灵, 等. 基于循证理念制定卫生健康标准的思考[J]. 协和医学杂志, 2024, 15(2): 435-441.
[36]	Kamath U, Keenan K, Somers G, et al. Large language models : a deep dive : bridging theory and practice[M]. Springer: Cham, 2024: 135-175.
[37]	贾洪波, 王虎峰, 农静雅. 基于健康中国战略的卫生健康标准化建设:成效、挑战与着力点[J]. 学习论坛, 2025(2): 65-73.
[38]	易磊, 杨忠. 生成式人工智能数据安全风险评估的标准化研究[J]. 标准科学, 2026(2): 6-15, 32.
[39]	翟运开, 郭瑞芳, 王宇, 等. 数据生命周期视角下的医疗健康大数据质量评价研究[J]. 现代情报, 2024, 44(1): 116-129.
[40]	王玲, 熊维, 荣凌, 等. 健康医疗数据共享背景下中国患者隐私保护相关研究现状[J]. 中国医学伦理学, 2024, 37(7): 778-784.
[41]	郭德忠, 张云蔚.生成式人工智能训练数据侵权风险与法律应对[J]. 湘潭大学学报(哲学社会科学版), 2024, 48(5): 78-86.
[42]	弓孟春, 潘慧, 刘辉, 等. 医学生人工智能素养能力清单与测评框架专家共识(2025年版)[J]. 中国医学科学院学报, 2026, 48(1): 13-23.

生成式人工智能赋能卫生健康标准体系构建现状与挑战

The Current Situation and Challenges of Building a Standards System for High-quality Development of Health Care Empowered by Generative Artificial Intelligence

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