Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders

References

1. 1.
   1. Boyko JA,
   2. Lavis JN,
   3. Dobbins M

   . Deliberative dialogues as a strategy for system-level knowledge translation and exchange. Healthc Policy 2014;9:122–31.

   OpenUrl
2. 2.
   Government of Canada [Internet]. About primary health care; 2012 [cited 2020 Sep 4]. Available from: https://www.canada.ca/en/health-canada/services/primary- health-care/about-primary-health-care.html.
3. 3.
   1. Upshur R

   [Internet]. Artificial intelligence, machine learning and the potential impacts on the practice of family medicine: a briefing document; 2019. Available from: http://www.ams-inc.on.ca/wp-content/uploads/2019/05/AMS-CFPC-paper-PRINT.pdf.
4. 4.
   1. Hutson M

   . AI glossary: artificial intelligence, in so many words. Science \2017;357:19.

   OpenUrlAbstract/FREE Full Text
5. 5.
   1. Shaw J,
   2. Rudzicz F,
   3. Jamieson T,
   4. Goldfarb A

   . Artificial intelligence and the implementation challenge. J Med Internet Res 2019;21:e13659.

   OpenUrlCrossRefPubMedWeb of Science
6. 6.
   1. Buchanan BG

   . A (very) brief history of artificial intelligence. AI Mag 2005;26:53.

   OpenUrl
7. 7.
   1. Davidson L

   [Internet]. Narrow vs. general AI: what's next for artificial intelligence?; 2019 [cited 2020 Sep 4]. Available from: https://www.springboard.com/blog/narrow-vs-general-ai/#:∼:text=Narrow AI (ANI) is defined,and advances in that spectrum.
8. 8.
   1. Beam AL,
   2. Kohane IS

   . Big data and machine learning in health care. JAMA 2018;319:1317–8.

   OpenUrlCrossRefPubMed
9. 9.
   1. Teng J,
   2. Burgess M,
   3. McGrail K,
   4. Bentley C,
   5. O'Doherty K

   [Internet]. Research data use in a digital society: a deliberative public engagement; 2019. Available from: https://cpb-ca-c1.wpmucdn.com/sites.uoguelph.ca/dist/0/126/files/2021/11/Data-Deliberation-2019-Booklet.pdf.
10. 10.
    1. Lecun Y,
    2. Bengio Y,
    3. Hinton G

    . Deep learning. Nature 2015;521:436–44.

    OpenUrlCrossRefPubMedWeb of Science
11. 11.
    1. Naylor CD

    . On the prospects for a (deep) learning health care system. JAMA 2018;320:1099–1100.

    OpenUrlCrossRef
12. 12.
    1. Esteva A,
    2. Kuprel B,
    3. Novoa RA,
    4. et al

    . Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–8.

    OpenUrlCrossRefPubMed
13. 13.
    1. Topol EJ

    . High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44–56.

    OpenUrlCrossRef
14. 14.
    1. Rajkomar A,
    2. Oren E,
    3. Chen K,
    4. et al

    . Scalable and accurate deep learning for electronic health records. NPJ Digit Med [Internet] 2018;1–10.
15. 15.
    1. Chang F,
    2. Gupta N

    . Progress in electronic medical record adoption in Canada. Can Fam Physician 2015;61:1076–84.

    OpenUrl
16. 16.
    1. Kueper JK,
    2. Terry AL,
    3. Zwarenstein M,
    4. Lizotte DJ

    . Artificial intelligence and primary care research: a scoping review. Ann Fam Med 2020;18:250–258.

    OpenUrlCrossRef
17. 17.
    1. Hill RG,
    2. Sears LM,
    3. Melanson SW

    . 4000 clicks: a productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med 2013;31:1591–4.

    OpenUrl
18. 18.
    1. Wachter R,
    2. Goldsmith J

    [Internet]. To combat physician burnout and improve care, fix the electronic health record; 2018.: Available from: http://0- search.ebscohost.com.olinkserver.franklin.edu/login.aspx?direct=true&db=buh&A N=128881532&site=eds-live.
19. 19.
    1. Abràmoff MD,
    2. Lavin PT,
    3. Birch M,
    4. Shah N,
    5. Folk JC

    . Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39.

    OpenUrl
20. 20.
    1. Marcus JL,
    2. Hurley LB,
    3. Krakower DS,
    4. Alexeeff S,
    5. Silverberg MJ,
    6. Volk JE

    . Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. Lancet HIV 2019;6:e688–e695.

    OpenUrlAbstract/FREE Full Text
21. 21.
    Saykara AI assistant for physicians [Internet]; 2020 [cited 2020 Sep 5]. Available from: https://www.saykara.com/.
22. 22.
    1. Wiens J,
    2. Saria S,
    3. Sendak M,
    4. et al

    . Do no harm: a roadmap for responsible machine learning for health care. Nat Med 2019;25:1337–40.

    OpenUrlCrossRefPubMed
23. 23.
    1. Obermeyer Z,
    2. Powers B,
    3. Vogeli C,
    4. Mullainathan S

    . Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366:447–53.

    OpenUrl