El-Shazly et al
Greener Journal of Medical Sciences
Vol. 11(1), pp. 37-45, 2021
Copyright ©2021, the copyright of this article is retained by the author(s)
Primary health care physicians’ perception of artificial intelligence in health care
Medhat El-Shazly1; Maha Al-Majed2; Ahmad Al-Omar3; Marwa Abdalla4; Maria Pereira5
1 MD, Consultant of Public Health, MOH, Kuwait and Professor of Health Statistics, Medical Research Institute, Alexandria University, Egypt.
2 MSc, Specialist of Public Health, Head of Planning and Health System Research Department, MOH, Kuwait
3 MRCGP, Specialist, Health System Research Department, MOH, Kuwait
4 MSc, Department of Planning and Follow-up, MOH, Kuwait
5 Secretary, Department of Planning and Follow-up, MOH, Kuwait
Article No.: 032821030
Background: Artificial intelligence (AI), broadly defined as the imitation of human cognition by a machine. Despite the early success of AI in performing and assisting with clinical tasks, there is still skepticism about the potential of this technology.
Objectives: The aim of this study is to highlight primary health care (PHC) physician’s perception towards AI.
Subjects and Methods: The study design is a cross-sectional multicenter using predesigned self-administered questionnaire to collect data from all the physicians working in 41 PHC centers. The questionnaire included personal data as well as physician’s perception of AI regarding using in PHC, fields of application, possible risks, and their opinion if AI would replace physicians in various tasks of PHC. Simple descriptive statistics were used (mean + standard deviation for quantitative variables, and frequency with percentage distribution for categorized variables).
Results: Within the 531 physicians working in the selected settings and invited to participate in the study, 369 responded with a response rate of 69.5%. Within the participants, 37.3% were males, 42.9% were < 35 years old, 57.1% were Kuwaitis. Less than half of the participating physicians (46.2%) considered themselves familiar with AI. A higher proportion of physicians (71.7%) believed that AI had useful applications in the medical field, only 11.8% agreed that the diagnostic ability of AI is superior to the clinical experience of a human doctor and only 5.0% agreed that AI could replace their jobs. When physicians were asked about the most useful expected application of AI in the field of medicine, came at the top of the list biopharmaceutical research and development (30.5%) followed by diagnosis (24.1%). The proportion of physicians who believed that it is likely that technology will be able to fully replace human physicians was 79.5% regarding documentation about patient, 55.5% regarding establishing prognoses, 45.9% for referral of patients, and 44.0% for diagnosis.
Conclusion: Despite the agreement among participants regarding the impact of AI on the health care system, physicians still have skepticism about the potential of this technology and demonstrate a more negative attitude.
Prof. Dr. Medhat El-Shazly
PHC; physicians; Perception; AI
Bakkar N, Kovalik T, Lorenzini I, Spangler S, Lacoste A, Sponaugle K, Ferrante P, Argentinis F, Sattler R, Bowser R. (2018). Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol; 135(2) :227-47.
Blease C, Bernstein MH, Gaab J, Kaptchuk YJ, Kossowsky J, Mandl KD, Davis RB. DesRoches CM. (2018). Computerization and the future of primary care: A survey of general practitioners in the UK. PLOS ONE | https://doi.org/10.1371/journal.pone.0207418
Darcy AM, Louie AK, Roberts LW. (2016). Machine learning and the profession of medicine. JAMA, 315(6), 551–2.
Doraiswamy M, Blease C, Bodner K. (2020). Artificial intelligence and the future of psychiatry: Insights from a global physician survey (2020). Artificial Intelligence in Medicine; 102. https://doi.org/10.1016/j.artmed.2019.101753
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. (2019). A guide to deep learning in healthcare. Nat Med;25(1): 4-29.
Inkster B, Sarda S, Subramanian V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth;6(11):e12106. [doi: 10.2196/12106]
James R. National Institute of on Minority Health and Health Disparities. (2017). National Institutes of Health Precision medicine initiative URL: https://www.nimhd.nih.gov/about/legislative-info/clips/pmi.html [accessed 2019-02-05] [WebCite Cache ID 75yNuNGg5]
Jha S (2016). Will computer replace radiologist? https://www.medscape.com/viewarticle/863127
Kent J ( (2018). Providers Embrace Predictive Analytics for Clinical, Financial Benefits". Health IT Analytics. https://healthitanalytics.com/news/providers-embrace-predictive-analytics-for-clinical-financial-benefits
Krittanawong C. (2018).. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med; 48: e13-e14. [doi: 10.1016/j.ejim.2017.06.017] [Medline: 28651747]
Legg S, Hutter M. (2007). A Collection of Definitions of Intelligence (Technical report). IDSIA. https://arXiv:0706.3639. Bibcode:2007arXiv0706.3639L. 07-07.
Mandl KD, Bourgeois FT. (2017). The Evolution of patient diagnosis: From art to digital data-driven science. Jama; 318(19): 1859–60
Naylor CD. (2018). On the prospects for a deep learning health care system. JAMA, 320(11): 1099-100.
Nedelkoska L, Quintini G. (2018). Automation, skills use and training. OECD Social, Employment and Migration Working Papers, 2018. OECD Publishing, Paris, 202, 1- 124.
Obermeyer Z., Lee T. H. (2017). Lost in thought – the limits of the human mind and the future of medicine. New England Journal of Medicine, 377(13), 1209-1211.
Oh S, Kim JH, Choi S, Lee HJ, Hong J, Kwon SH. (2019). Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J Med Internet Res; 21(3): e12422) doi:10.2196/12422
Russell S, Norvig P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). http://aima.cs.berkeley.edu/.
Sarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R, Diamandis P. (2019). Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPG Digit Med; 2: 28 https://www.nature.com/articles/s41746-019-0106-0
Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart; 104(14): 1156-64.
Topol, E. (2019). Deep medicine: how artificial intelligence can make healthcare human again. Basic Books. Hachette UK
Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. (2018). Automatic determination of the need for intravenous contrast in musculoskeletal MRI examinations using IBM Watson's natural language processing algorithm. J Digit Imaging;31(2):245-251.
Verghese A, Shah NH, Harrington RA. (2018). What this computer needs is a Physician: Humanism and AI. JAMA, 2, 319-21.
Walsh T. (2017). Android dreams: the past, present, and future of artificial intelligence. UK: Hurst.
Cite this Article: El-Shazly M; Al-Majed M; Al-Omar A; Abdalla M; Pereira M (2021). Primary health care physicians’ perception of artificial intelligence in health care. Greener Journal of Medical Sciences, 11(1): 37-45.
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