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Universal health coverage in China part 1: progress and gaps.中国的全民健康覆盖 1:进展与差距。
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大型语言模型在初级卫生保健中的机遇与挑战。

Opportunities and Challenges for Large Language Models in Primary Health Care.

作者信息

Qin Hongyang, Tong Yuling

机构信息

The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.

Beigan Street Community Health Service Center, Xiaoshan District, Hangzhou, China.

出版信息

J Prim Care Community Health. 2025 Jan-Dec;16:21501319241312571. doi: 10.1177/21501319241312571. Epub 2025 Mar 31.

DOI:10.1177/21501319241312571
PMID:40162893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11960148/
Abstract

Primary Health Care (PHC) is the cornerstone of the global health care system and the primary objective for achieving universal health coverage. China's PHC system faces several challenges, including uneven distribution of medical resources, a lack of qualified primary healthcare personnel, an ineffective implementation of the hierarchical medical treatment, and a serious situation regarding the prevention and control of chronic diseases. The rapid advancement of artificial intelligence (AI) technology, large language models (LLMs) demonstrate significant potential in the medical field with their powerful natural language processing and reasoning capabilities, especially in PHC. This review focuses on the various potential applications of LLMs in China's PHC, including health promotion and disease prevention, medical consultation and health management, diagnosis and triage, chronic disease management, and mental health support. Additionally, pragmatic obstacles were analyzed, such as transparency, outcomes misrepresentation, privacy concerns, and social biases. Future development should emphasize interdisciplinary collaboration and resource sharing, ongoing improvements in health equity, and innovative advancements in medical large models. There is a demand to establish a safe, effective, equitable, and flexible ethical and legal framework, along with a robust accountability mechanism, to support the achievement of universal health coverage.

摘要

初级卫生保健(PHC)是全球卫生保健系统的基石,也是实现全民健康覆盖的首要目标。中国的初级卫生保健系统面临着诸多挑战,包括医疗资源分配不均、缺乏合格的基层医疗人员、分级诊疗实施不力以及慢性病防控形势严峻。随着人工智能(AI)技术的迅速发展,大语言模型(LLMs)凭借其强大的自然语言处理和推理能力在医疗领域展现出巨大潜力,尤其是在初级卫生保健方面。本综述聚焦于大语言模型在中国初级卫生保健中的各种潜在应用,包括健康促进与疾病预防、医疗咨询与健康管理、诊断与分诊、慢性病管理以及心理健康支持。此外,还分析了一些实际存在的障碍,如透明度问题、结果误传、隐私担忧和社会偏见。未来的发展应强调跨学科合作与资源共享、持续改善健康公平性以及医疗大模型的创新进步。需要建立一个安全、有效、公平且灵活的伦理和法律框架,以及一个强有力的问责机制,以支持全民健康覆盖目标的实现。