Your browser doesn't support javascript.
loading
Artificial Intelligence and Primary Care Research: A Scoping Review.
Kueper, Jacqueline K; Terry, Amanda L; Zwarenstein, Merrick; Lizotte, Daniel J.
Afiliação
  • Kueper JK; Departments of Epidemiology & Biostatistics and Computer Science, Western University, London, Ontario, Canada jkueper@uwo.ca.
  • Terry AL; Departments of Epidemiology & Biostatistics, Family Medicine, Schulich Interfaculty Program in Public Health, Western University, London, Ontario, Canada.
  • Zwarenstein M; Departments of Epidemiology & Biostatistics and Family Medicine, Western University, London, Ontario, Canada.
  • Lizotte DJ; Departments of Epidemiology & Biostatistics, Computer Science, Schulich Interfaculty Program in Public Health, Statistical & Actuarial Sciences, Western University, London, Ontario, Canada.
Ann Fam Med ; 18(3): 250-258, 2020 05.
Article em En | MEDLINE | ID: mdl-32393561
PURPOSE: Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODS: We performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in Eng-lish. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTS: Of 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%). CONCLUSIONS: Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atenção Primária à Saúde / Inteligência Artificial / Pesquisa Interdisciplinar Tipo de estudo: Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atenção Primária à Saúde / Inteligência Artificial / Pesquisa Interdisciplinar Tipo de estudo: Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article