中国全科医学2025,Vol.28Issue(1) :1-6.DOI:10.12114/j.issn.1007-9572.2024.0277

人工智能大语言模型在基层医疗卫生服务中的应用与挑战

The Application of Large Language Models in Primary Healthcare Services and the Challenges

闫温馨 胡健 曾华堂 刘民 梁万年
中国全科医学2025,Vol.28Issue(1) :1-6.DOI:10.12114/j.issn.1007-9572.2024.0277

人工智能大语言模型在基层医疗卫生服务中的应用与挑战

The Application of Large Language Models in Primary Healthcare Services and the Challenges

闫温馨 1胡健 1曾华堂 2刘民 3梁万年4
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作者信息

  • 1. 100084 北京市,清华大学万科公共卫生与健康学院
  • 2. 518028 广东省深圳市卫生健康发展研究和数据管理中心
  • 3. 100191 北京市,北京大学公共卫生学院
  • 4. 100084 北京市,清华大学万科公共卫生与健康学院;100084 北京市,清华大学健康中国研究院
  • 折叠

摘要

基层医疗系统是实现健康公平的关键.我国面临医疗资源分布不平衡、基层医生数量短缺以及慢性病防治形势不容乐观的严峻挑战.人工智能大语言模型在医疗系统中发挥出了强大优势,本文深入探讨了大模型在基层医疗系统中的应用及其面临的挑战,提出应进一步深化大模型的应用,以辅助基层医生常见病诊疗,推动智能化健康教育和慢性病管理,托底"老少边穷"地区基层卫生服务,激发全科医学的飞跃性发展,并推进大模型在全科诊疗与基层卫生服务中的产业化,为健康中国建设提供重要支撑.

Abstract

The primary healthcare system is key to achieving a health equity. In China,great obstacles are challenged by imbalanced medical resources,shortage of primary healthcare providers,and the prevention and treatment of chronic diseases. Artificial intelligence large language models have demonstrated strong advantages in the medical system. This article deeply explored the application of large language models in the primary healthcare system and the challenges. The large language models are expected to assist the diagnosis and treatment of common diseases in grassroot medical institutions,promote intelligent health education and chronic disease management,underpin primary health services in the undeveloped and remote areas,stimulate the leapfrog development of general medicine,and accelerate the industrialization of large language models in general diagnosis and treatment and primary health services,thus providing important support for the construction of healthy China.

关键词

大语言模型/全科医学/基层医疗卫生服务/健康公平/健康教育/慢性病管理

Key words

Large language model/General practice/Primary healthcare services/Health equity/Health education/Chronic disease management

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出版年

2025
中国全科医学
中国医院协会

中国全科医学

北大核心
影响因子:2.04
ISSN:1007-9572
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