Artificial intelligence (AI) has substantial potential to improve service quality and health equity in primary healthcare institutions. China has introduced a series of policies to promote the development and application of AI in primary care. However, health data poverty, defined as the inability of individuals or populations to benefit from technological innovation because of insufficiently representative data, may widen service gaps between hospitals and primary healthcare institutions, as well as across primary healthcare institutions. To date, evidence on AI applications in China's primary healthcare institutions and their potential implications for health data poverty has not been systematically mapped.

To characterize AI models developed or validated using data from China's primary healthcare institutions, describe the datasets used in these studies, and assess the severity and distribution of health data poverty in this context.

We searched PubMed, Embase, Web of Science, CNKI and Wanfang Data for studies published between 2009 and 2025 on AI models in China's primary healthcare institutions. Data were extracted and synthesized across three domains: model characteristics, including algorithm type, validation strategy and performance; dataset characteristics, including data source, acquisition method and data content; and metadata distribution, including population coverage and handling of missing data.

57 studies were included. Existing AI models for primary care in China were mainly based on machine learning or deep learning and covered multiple application scenarios, including disease prediction and diagnosis. The most common disease areas were endocrine disorders, mainly diabetes, cardiovascular diseases, mainly hypertension, and mental health conditions. Datasets used to develop or validate these models showed limited accessibility, with no fully open dataset identified. Reporting quality was suboptimal, with a mean score of 6.53 out of 10; nearly one-third of studies did not report inclusion and exclusion criteria or methods for handling missing data. Dataset representativeness was also limited, with people younger than 45 years accounting for only 13.5% of reported study populations (5/37), and studies were predominantly conducted in more developed regions.

AI research in China's primary healthcare institutions is constrained by health data poverty, including limited data accessibility, suboptimal reporting quality and insufficient representativeness. Strengthening data linkage, developing high-quality research-ready datasets and establishing end-to-end governance may help build a stronger data foundation for primary healthcare.

人工智能（AI）在提高基层医疗卫生机构服务质量和健康公平方面发挥着重要作用，我国已出台多项政策鼓励AI在基层医疗卫生机构的发展应用。然而，健康数据贫困，即"由于缺乏代表性数据，从而使得个体或人群难以从技术创新中获益"问题，有可能加剧基层医疗卫生机构与医院，以及基层医疗卫生机构之间的医疗服务差距。目前，尚缺乏我国基层医疗卫生机构AI应用现状及其潜在健康数据贫困问题的系统梳理。

梳理基于我国基层医疗数据开发或验证的AI模型及使用数据库的现状、特征及质量，分析我国基层医疗健康数据贫困问题的严重程度及分布特征。

通过PubMed、Embase、Web of Science、中国知网、万方数据知识服务平台检索2009—2025年我国基层医疗卫生机构AI模型研究，从模型基本特征（算法类型、验证方式及结果等）、数据库基本特征（收集地点、获取方式、数据库内容等）、元数据分布（覆盖人群、缺失值处理等）3方面进行数据提取与综述。

最终纳入57篇文章。我国现有基层AI模型以机器学习、深度学习为主，涉及疾病预测、诊断等多个应用场景，疾病以内分泌类（糖尿病）、心血管类（高血压）、精神类为主。用于基层人工智能构建/验证的数据库存在可及性不足（无完全公开数据集）、报告质量不佳（平均得分6.95分，总分10分，近1/3未报告纳入及排除标准、缺失值处理方法）、样本代表性不足[<45岁人群占比13.5%（5/37），发达地区主导]等问题。

推动卫生系统数据联动、设立高质量研究型数据集、建立全过程监管体系是解决我国基层医疗卫生机构健康数据贫困问题的有效方式，可为支持基层医疗服务体系高质量发展提供基础数据支撑。