[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3668":3,"related-3668":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":40,"slug":41,"view_count":42,"doi":43,"paper":44,"created_at":67},3668,"Artificial intelligence and machine learning for organizational and system-level functions in primary health care: A scoping review protocol","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0329426","OBJECTIVE: Primary health care (PHC) is the cornerstone of health systems worldwide, yet faces growing pressures from aging populations, workforce shortages, and constrained resources. While artificial intelligence (AI) and machine learning (ML) are increasingly used for clinical tasks such as diagnostics and decision support, their potential to address organizational and system-level processes remains underexplored. This scoping review maps AI and ML applications targeting non-clinical, organizational, and system-level functions in PHC, examining techniques employed, application purposes, data sources, and implementation maturity, and identifying evidence gaps to inform future research. METHODS: This scoping review will be conducted using the Arksey and O'Malley framework and reported according to PRISMA-ScR. We will search Ovid MEDLINE, EBSCO CINAHL, Ovid Embase, Cochrane Library (Wiley), Ovid PsycINFO, and IEEE Xplore, as well as grey literature sources, from January 2010 to December 2025, with a pre-submission update. Two independent reviewers will screen titles, abstracts, and full texts. Eligible studies include empirical research describing, evaluating, implementing, or developing AI and ML applications at the meso-level (organizational, e.g., care scheduling, population stratification) and macro-level (system-wide, e.g., workforce planning, funding allocation) of PHC. Studies on micro-level clinical applications, non-empirical research, and secondary literature will be excluded. Applications will be classified using a two-layer taxonomy (AI technique family and core algorithm) and mapped to PHC building blocks adapted from WHO operational frameworks. Synthesis will examine technique-building block intersections, application purposes, data sources, implementation maturity, and evidence gaps. The protocol is registered in Open Science Framework (osf.io\u002Fwzj5x). CONCLUSIONS: This review will provide a timely synthesis of AI and ML applications at organizational and system levels of PHC, identifying which building blocks have been addressed, which remain underexplored, and where the field stands in terms of implementation readiness. The findings will inform a targeted research agenda and provide actionable evidence for decision-makers seeking to leverage AI and ML for PHC strengthening.","目的：初级卫生保健（PHC）是全球卫生体系的基石，然而正面临人口老龄化、劳动力短缺和资源受限带来的日益增大的压力。尽管人工智能（AI）和机器学习（ML）越来越多地应用于诊断和决策支持等临床任务，但其在解决组织层面和系统层面流程方面的潜力仍未得到充分探索。本范围综述旨在梳理针对PHC中非临床、组织及系统层面功能的AI和ML应用，考察所采用的技术、应用目的、数据来源和实施成熟度，并识别证据缺口以指导未来研究。方法：本范围综述将采用Arksey和O'Malley框架进行，并按照PRISMA-ScR报告。我们将检索Ovid MEDLINE、EBSCO CINAHL、Ovid Embase、Cochrane Library（Wiley）、Ovid PsycINFO和IEEE Xplore，以及灰色文献来源，检索时间范围为2010年1月至2025年12月，并在投稿前进行更新检索。两名独立评审员将筛选标题、摘要和全文。纳入标准为描述、评估、实施或开发PHC中观层面（组织层面，如照护排程、人群分层）和宏观层面（系统层面，如劳动力规划、资金分配）AI和ML应用的实证研究。微观层面临床应用研究、非实证研究和二次文献将被排除。应用将采用双层分类法（AI技术族和核心算法）进行分类，并映射至改编自WHO操作框架的PHC构建模块。综合分析将考察技术与构建模块的交叉点、应用目的、数据来源、实施成熟度和证据缺口。本方案已在开放科学框架（osf.io\u002Fwzj5x）注册。结论：本综述将对PHC组织和系统层面的AI和ML应用进行及时的综合梳理，识别哪些构建模块已被涉及、哪些仍未被充分探索，以及该领域在实施准备度方面的现状。研究结果将为有针对性的研究议程提供信息，并为寻求利用AI和ML加强PHC的决策者提供可操作的证据。",null,"PLoS ONE","2026-09-25T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为基层医疗中AI\u002FML组织与系统层面应用的范围综述方案，属医疗卫生信息化领域，与三农、农业信息化、智慧农业、数字乡村无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":39},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":35,"tags":38},"I2",25,true,[33,34],"machine learning","artificial intelligence",[36,33,34,37],"机器学习","decision support",[],"农业相关性需要复核",[],"Artificialintelligenceandmachinelearning-3668",3,"10.1371\u002Fjournal.pone.0329426",{"doi":43,"openalex_id":45,"authors":46,"venue":10,"cited_by_count":15,"oa_url":58,"card":59,"direction":65,"ingested_from":66},"W7214380247",[47,50,53,55],{"name":48,"orcid":49},"Pablo Gálvez-Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-6268-559X",{"name":51,"orcid":52},"Li‐Anne Audet","https:\u002F\u002Forcid.org\u002F0000-0002-7805-993X",{"name":54,"orcid":9},"Zahra Shakeri",{"name":56,"orcid":57},"Walter P. Wodchis","https:\u002F\u002Forcid.org\u002F0000-0003-2494-7031","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0329426&type=printable",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"综述方案：梳理AI\u002FML在初级卫生保健组织与系统层面的非临床应用。","遵循Arksey和O'Malley框架与PRISMA-ScR，检索六大数据库及灰","方案将映射AI技术到WHO卫生系统模块，识别证据空白与实施成熟度。","其他","可将该组织\u002F系统层面AI综述框架迁移至农业信息化，研究AI在农业组织与政策层面的应用空白。","农业遥感与作物表型","openalex","2026-09-28T23:30:25.467244Z",{"total":15,"page":69,"page_size":69,"items":70},1,[]]