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Review ArticleSystematic Review

Artificial Intelligence and Primary Care Research: A Scoping Review

Jacqueline K. Kueper, Amanda L. Terry, Merrick Zwarenstein and Daniel J. Lizotte
The Annals of Family Medicine May 2020, 18 (3) 250-258; DOI: https://doi.org/10.1370/afm.2518
Jacqueline K. Kueper
1Departments of Epidemiology & Biostatistics and Computer Science, Western University, London, Ontario, Canada
MSc
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  • For correspondence: jkueper@uwo.ca
Amanda L. Terry
2Departments of Epidemiology & Biostatistics, Family Medicine, Schulich Interfaculty Program in Public Health, Western University, London, Ontario, Canada
PhD
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Merrick Zwarenstein
3Departments of Epidemiology & Biostatistics and Family Medicine, Western University, London, Ontario, Canada
MBBCh, PhD
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Daniel J. Lizotte
4Departments of Epidemiology & Biostatistics, Computer Science, Schulich Interfaculty Program in Public Health, Statistical & Actuarial Sciences, Western University, London, Ontario, Canada
PhD
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  • Applications of Artificial Intelligence in Primary Care: Research to Practice
    Md Mahbub Hossain
    Published on: 16 June 2020
  • Published on: (16 June 2020)
    Page navigation anchor for Applications of Artificial Intelligence in Primary Care: Research to Practice
    Applications of Artificial Intelligence in Primary Care: Research to Practice
    • Md Mahbub Hossain, Public Health Physician, Texas A&M School of Public Health, College Station, TX 77843, USA.

    This study provides meaningful insights into the current state of research that bridges two critical concepts: primary care and artificial intelligence AI. Perhaps the need for understanding these two concepts is critical, especially in the context of the coronavirus disease (COVID-19) pandemic. Globally, countries that are fighting strongly against COVID-19 have at least two beautiful things in common--they have a strong framework for preventive measures, and they are using data at their best. The cases of South Korea or New Zealand tell us why we need to invest in primary care and leverage data sciences to prevent deaths across populations.

    The findings of this study highlight major domains of research in primary care settings that used AI. However, some of the findings tell us more about global health disparity in this area. As most studies were conducted in high-income countries, there is a critical gap of AI-based research on primary care in low- and middle-income countries. In those countries, people rely on primary care, and most of these countries have a large population, which offers a great opportunity to leverage AI for generating meaningful insights for improved policymaking and practice.

    Unfortunately, not many countries have the technological capacities or data infrastructure to follow this path. This is a blind spot where the global health community can invest. It is essential to build strategic partnerships and mobilize the global resources t...

    Show More

    This study provides meaningful insights into the current state of research that bridges two critical concepts: primary care and artificial intelligence AI. Perhaps the need for understanding these two concepts is critical, especially in the context of the coronavirus disease (COVID-19) pandemic. Globally, countries that are fighting strongly against COVID-19 have at least two beautiful things in common--they have a strong framework for preventive measures, and they are using data at their best. The cases of South Korea or New Zealand tell us why we need to invest in primary care and leverage data sciences to prevent deaths across populations.

    The findings of this study highlight major domains of research in primary care settings that used AI. However, some of the findings tell us more about global health disparity in this area. As most studies were conducted in high-income countries, there is a critical gap of AI-based research on primary care in low- and middle-income countries. In those countries, people rely on primary care, and most of these countries have a large population, which offers a great opportunity to leverage AI for generating meaningful insights for improved policymaking and practice.

    Unfortunately, not many countries have the technological capacities or data infrastructure to follow this path. This is a blind spot where the global health community can invest. It is essential to build strategic partnerships and mobilize the global resources that may help nations to help each other to advance science and practice in this area. Primary care research in developing countries using AI can inform critical gaps and facilitate informed decision-making.

    The authors rightly concluded that “AI for primary care is an innovation that is in early stages of maturity, with few tools ready for widespread implementation. Interdisciplinary research teams, including frontline clinicians and evaluation studies in primary care settings, will be crucial for the advancement and success of this field.” I extend this message and call for an international collaboration that may help local, regional, and global healthcare stakeholders to identify and address health problems in respective contexts. Perhaps that will also contribute to better surveillance of future pandemics and primary preventions through extensive networks of primary care. Nonetheless, widespread use of AI in primary care can foster health systems strengthening resulting in an improved continuum of care for patients right from the beginning of their interaction with the health systems as envisioned in the Alma Ata Declaration of 1978.

    Show Less
    Competing Interests: None declared.
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The Annals of Family Medicine: 18 (3)
The Annals of Family Medicine: 18 (3)
Vol. 18, Issue 3
May/June 2020
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Artificial Intelligence and Primary Care Research: A Scoping Review
Jacqueline K. Kueper, Amanda L. Terry, Merrick Zwarenstein, Daniel J. Lizotte
The Annals of Family Medicine May 2020, 18 (3) 250-258; DOI: 10.1370/afm.2518

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Artificial Intelligence and Primary Care Research: A Scoping Review
Jacqueline K. Kueper, Amanda L. Terry, Merrick Zwarenstein, Daniel J. Lizotte
The Annals of Family Medicine May 2020, 18 (3) 250-258; DOI: 10.1370/afm.2518
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  • What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
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  • Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres
  • Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders
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  • A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data
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Subjects

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Keywords

  • artificial intelligence
  • health information technology
  • health informatics
  • electronic health records
  • big data
  • data mining
  • primary care
  • family medicine
  • decision support
  • diagnosis
  • treatment
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