Accepted for/Published in: JMIR Medical Education
Date Submitted: Sep 13, 2022
Open Peer Review Period: Sep 12, 2022 - Nov 7, 2022
Date Accepted: Jan 15, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Computerization of GPs’ jobs: A Mixed Methods Survey of Final Year Medical Students in Ireland
ABSTRACT
Background:
The potential for digital health technologies, including machine learning-enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics.
Objective:
To describe the opinions of final year medical students in Ireland regarding the potential of future technology to replace, and work alongside, general practitioners (GPs) in performing key tasks.
Methods:
Between March 2019 to April 2020, using a cross-sectional design, we conducted a mixed methods paper-based survey of final year medical students. The survey was administered at four out of seven medical schools in Ireland across each of the four provinces in the country.
Results:
In total 252 of 585 (43%) of final year students at three medical schools responded, and data collection at one medical school was terminated due to the disruption of COVID-19. Forecasting the potential impact of artificial intelligence/machine learning on primary care 25 years from now, around half of all surveyed students believed the job of GPs will change minimally or there will be no change. Notably, students who did not intend to enter primary care predicted AI/ML would have greater impact on the job of GPs.
Conclusions:
We caution that without a firm curricular foundation on advances in AI/ML students may rely on extreme perspectives – self-preserving optimism biases which demote the impact of advances of technologies on primary care, on the one hand, or techno-hype on the other. Ultimately, both approaches may lead to negative consequences in healthcare. Improvements in medical education could better help prepare tomorrow’s doctors to optimize the ethical, evidence-based implementation of AI/ML-enabled tools in medicine to enhance the care of tomorrow’s patients.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.