%0期刊文章%@ 1438-8871 %I JMIR出版物%V 23%卡塔尔世界杯8强波胆分析 N 9% P e29839 %T人工智能在社区初级卫生保健中的应用:系统范围审查和关键评估%A Abbasgholizadeh Rahimi,Samira %A Légaré，France %A Sharma,Gauri %A Archambault,Patrick %A Zomahoun,Herve Tchala Vignon %A Chandavong,Sam %A Rheault,Nathalie %A T Wong,Sabrina %A Langlois,Lyse %A Couturier,Yves %A Salmeron,Jose L %A Gagnon,Marie-Pierre %A Légaré，Jean %+麦吉尔大学医学和健康科学学院家庭医学系，5858 Côte-des-Neiges路，Suite 300, Montreal, QC，加拿大，1 514 399 9218，samira.rahimi@mcgill.ca %K人工智能%K机器学习%K基于社区的初级卫生保健%K系统范围综述%D 2021 %7 3.9.2021 %9综述%J J医学互联网Res %G英文%X背景:关于将人工智能(AI)集成到基于社区的初级卫生保健(CBPHC)的研究强调了实践中的几个优点和缺点，例如，促进诊断和疾病管理，以及对这种集成的意外有害影响的怀疑。然而，缺乏关于全面知识综合的证据，可以阐明在CBPHC中测试或实现的AI系统。目的:我们旨在识别和评估已发表的在CBPHC环境中测试或实施AI的研究。方法:我们根据早期研究和乔安娜布里格斯研究所(Joanna Briggs Institute, JBI)范围综述框架进行了系统范围综述，并根据PRISMA-ScR(系统综述和元分析范围综述首选报告项目)报告指南报告了研究结果。一名信息专家从研究开始之日起至2020年2月，在7个书目数据库中进行了全面搜索:Cochrane图书馆、MEDLINE、EMBASE、Web of Science、护理和联合健康文献累积索引(CINAHL)、ScienceDirect和IEEE Xplore。所选研究考虑了在CBPHC环境中提供和接受护理的所有人群，已经实施、测试或两者兼有的人工智能干预措施，并评估了与患者、卫生保健提供者或CBPHC系统相关的结果。使用预测模型偏倚风险评估工具(PROBAST)评估偏倚风险。 Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. Results: We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). Conclusions: We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings. %M 34477556 %R 10.2196/29839 %U //www.mybigtv.com/2021/9/e29839 %U https://doi.org/10.2196/29839 %U http://www.ncbi.nlm.nih.gov/pubmed/34477556