文献检索，用中文搜 PubMed

BACKGROUND

Artificial Intelligence (AI) has significantly reshaped Primary Health Care (PHC), offering various possibilities and complexities across all functional dimensions. The objective is to review and synthesize available evidence on the opportunities, challenges, and requirements of AI implementation in PHC based on the Primary Care Evaluation Tool (PCET).

METHODS

We conducted a systematic review, following the Cochrane Collaboration method, to identify the latest evidence regarding AI implementation in PHC. A comprehensive search across eight databases- PubMed, Web of Science, Scopus, Science Direct, Embase, CINAHL, IEEE, and Cochrane was conducted using MeSH terms alongside the SPIDER framework to pinpoint quantitative and qualitative literature published from 2000 to 2024. Two reviewers independently applied inclusion and exclusion criteria, guided by the SPIDER framework, to review full texts and extract data. We synthesized extracted data from the study characteristics, opportunities, challenges, and requirements, employing thematic-framework analysis, according to the PCET model. The quality of the studies was evaluated using the JBI critical appraisal tools.

RESULTS

In this review, we included a total of 109 articles, most of which were conducted in North America (n = 49, 44%), followed by Europe (n = 36, 33%). The included studies employed a diverse range of study designs. Using the PCET model, we categorized AI-related opportunities, challenges, and requirements across four key dimensions. The greatest opportunities for AI integration in PHC were centered on enhancing comprehensive service delivery, particularly by improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction. However, the most challenges emerged within the stewardship and resource generation functions, with key concerns related to data security and privacy, technical performance issues, and limitations in data accessibility. Ensuring successful AI integration requires a robust stewardship function, strategic investments in resource generation, and a collaborative approach that fosters co-development, scientific advancements, and continuous evaluation.

CONCLUSIONS

Successful AI integration in PHC requires a coordinated, multidimensional approach, with stewardship, resource generation, and financing playing key roles in enabling service delivery. Addressing existing knowledge gaps, examining interactions among these dimensions, and fostering a collaborative approach in developing AI solutions among stakeholders are essential steps toward achieving an equitable and efficient AI-driven PHC system.

PROTOCOL

Registered in Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/HG2DV ).

背景

人工智能（AI）已显著重塑初级卫生保健（PHC），在所有功能维度上带来了各种可能性和复杂性。目的是基于初级保健评估工具（PCET），回顾和综合关于在初级卫生保健中实施人工智能的机会、挑战和要求的现有证据。

方法

我们按照Cochrane协作方法进行了系统综述，以确定关于在初级卫生保健中实施人工智能的最新证据。使用医学主题词（MeSH）术语并结合SPIDER框架，对八个数据库——PubMed、科学网、Scopus、Science Direct、Embase、CINAHL、IEEE和Cochrane进行了全面检索，以找出2000年至2024年发表的定量和定性文献。两名评审员在SPIDER框架的指导下，独立应用纳入和排除标准，对全文进行评审并提取数据。我们根据PCET模型，采用主题框架分析方法，对从研究特征、机会、挑战和要求中提取的数据进行了综合。使用JBI批判性评价工具对研究质量进行了评估。

结果

在本次综述中，我们共纳入了109篇文章，其中大部分研究在北美进行（n = 49，44%），其次是欧洲（n = 36，33%）。纳入的研究采用了多种研究设计。使用PCET模型，我们在四个关键维度上对与人工智能相关的机会、挑战和要求进行了分类。人工智能融入初级卫生保健的最大机会集中在加强综合服务提供方面，特别是通过提高诊断准确性、优化筛查项目和推进疾病早期预测。然而，在管理和资源生成功能方面出现的挑战最多，主要问题涉及数据安全和隐私、技术性能问题以及数据可及性的限制。确保人工智能的成功融入需要强大的管理功能、对资源生成的战略投资以及促进共同开发、科学进步和持续评估的协作方法。