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AI and Primary Care: Scoping Review.

Gellert Katonai1,2,3, Nora Arvai1,3,4, Bertalan Mesko1,3

  • 1Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, Egyetem tér 1, Főépület földszint 15/A, Debrecen, 4032, Hungary, 36 52-258-010 ext. 58010.

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Summary

Artificial intelligence (AI) can enhance primary health care (PHC) but faces implementation barriers. Addressing usability, workflow, and equity is crucial for successful AI integration in generalist care settings.

Keywords:
AIAI integrationartificial intelligencedisease screeningdrug managementgeneral practiceintegrationprimary careprimary healthcarescoping reviewthematic analysis

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Area of Science:

  • Health Informatics
  • Primary Health Care Research
  • Artificial Intelligence in Medicine

Background:

  • Primary health care (PHC) is vital for accessible, continuous care but struggles with workforce shortages, administrative burdens, and increasing multimorbidity.
  • Artificial intelligence (AI) offers potential solutions for PHC by improving diagnostics, efficiency, and decision-making.
  • Existing research often neglects the practical application and user experiences of AI tools within primary care.

Purpose of the Study:

  • To conduct a scoping review of AI applications in PHC, focusing on empirical studies with direct stakeholder engagement.
  • To map AI tools within real-world primary care settings, examining clinical workflows and alignment with generalist care values.

Main Methods:

  • Searched PubMed, Web of Science, and Scopus databases (up to April 13, 2024) following Joanna Briggs Institute and PRISMA-ScR guidelines.
  • Included empirical, peer-reviewed studies (2010-2024) involving PHC stakeholders (GPs, nurses, patients) in real-world settings, evaluating AI applications.
  • Excluded algorithm-only validations, non-empirical research, and studies outside primary care contexts; used thematic analysis for synthesis.

Main Results:

  • 73 studies met inclusion criteria, categorized into decision support (29%), chronic disease management (22%), operations (16%), and acceptance/implementation (33%).
  • AI tools showed high technical accuracy, particularly in diagnostics.
  • Implementation faced significant barriers: usability issues, workflow misalignment, trust deficits, equity concerns, and financial constraints.

Conclusions:

  • AI has substantial potential to support PHC when integrated with clinical reasoning and relational care models.
  • Addressing barriers like usability, training, and workflow integration is essential for effective AI implementation.
  • Future research should focus on pragmatic trials, co-design with professionals, and responsible AI deployment strategies.