Artificial intelligence and primary care research: a scoping review
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Abstract
ABSTRACT Objective The purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care. Methods We performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI. Results Of 5,515 non-duplicate documents, 405 met our eligibility criteria. The body of literature is primarily focused on creating novel AI methods or modifying existing AI methods to support physician diagnostic or treatment recommendations, for chronic conditions, using data from higher income countries. Meaningfully more studies had at least one author with a technology, engineering, or math appointment than with a primary care appointment (57 (14%) compared to 217 (54%)). Predominant AI subfields were supervised machine learning and expert systems. Discussion Overall, AI research associated with primary care is at an early stage of maturity with respect to widespread implementation in practice settings. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed. SUMMARY BOXES Section 1: What is already known on this topic Advancements in technology and the availability of health data have increased opportunities for artificial intelligence to be used for primary care purposes. No comprehensive review of research on artificial intelligence associated with primary care has been performed. Section 2: What this study adds The body of research on artificial intelligence and primary care is driven by authors without appointments in primary care departments and is focused on developing artificial intelligence methods to support diagnostic and treatment decisions. There is a need for more interdisciplinary research teams and evaluation of artificial intelligence projects in ‘real world’ practice settings.
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Primary care has been slower to realize the benefits of AI.
“Primary care has been slower to realize the benefits of AI. [6][7][8]”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [7]
AI utilization in primary care clinics is in the early stages of development, with most studies focusing on AI methods to support physician clinical decision-making with diagnostic and treatment recommendations, particularly for chronic conditions.
“A recent scoping review found that AI utilization in primary care clinics (PCCs) is in the early stages of development, with most studies focusing on AI methods to support physician clinical decision-making (CDM) with diagnostic and treatment recommendations, particularly with management of chronic conditions.1,2”
From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [1]
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- Authors
- Kueper, J. K., Terry, A. L., Zwarenstein, M., & Lizotte, D. J.
- Affiliations
- Western University (CA)
- Corresponding author
- Kueper, Jacqueline K.
- Venue
- The Annals of Family Medicine
- Publisher
- Annals of Family Medicine
- Published
- 2020-05
- Volume
- 18
- Issue
- 3
- Pages
- 250-258
- ISSN
- 1544-1709 (ISSN-L)
- 1544-1717
- PMC
- PMC7213996
- OpenAlex
- W3022437576
- Cited by
- 143
- References
- 16
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 27 · 2023: 17 · 2022: 18 · 2021: 16 · 2020: 4
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The Annals of Family Medicine (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, primary care, machine learning, expert systems, clinical decision support, diagnostic support, treatment guidelines, chronic disease, end-user involvement, interdisciplinary research, research maturity
- MeSH terms
- Interdisciplinary Research, Artificial Intelligence, Humans, Primary Health Care
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