Artificial Intelligence and Primary Care Research: A Scoping Review.
artificial intelligence
big data
data mining
decision support
diagnosis
electronic health records
family medicine
health informatics
health information technology
primary care
scoping review
treatment
Journal
Annals of family medicine
ISSN: 1544-1717
Titre abrégé: Ann Fam Med
Pays: United States
ID NLM: 101167762
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
19
08
2019
revised:
11
11
2019
accepted:
21
11
2019
entrez:
13
5
2020
pubmed:
13
5
2020
medline:
28
4
2021
Statut:
ppublish
Résumé
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. 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). 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%). 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.
Identifiants
pubmed: 32393561
pii: 18/3/250
doi: 10.1370/afm.2518
pmc: PMC7213996
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
250-258Informations de copyright
© 2020 Annals of Family Medicine, Inc.
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