Accepted for/Published in: JMIR Medical Education
Date Submitted: Dec 9, 2021
Open Peer Review Period: Dec 9, 2021 - Feb 3, 2022
Date Accepted: May 27, 2022
(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Warning: This is an author submission that is not peer-reviewed or edited.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.
Needs, Challenges and Applications of Artificial Intelligence in Medical Education Curriculum
ABSTRACT
Artificial intelligence (AI) is on course to become a mainstay in the patient's room, physicians office and the surgical suite. Current advancements in healthcare technology put future physicians in an insufficiently equipped position and even possible inferiority to machines. Physicians will be regularly tasked with clinical decision making with the assistance of AI driven predictions. Present-day physicians are not trained to incorporate the suggestions of statistical predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their distribution of care. Medical schools do not currently incorporate AI in the curriculum due to the lack of faculty expertise or knowledge on the matter, the lack of evidence in students desire to learn about AI, complacency with an already rigorous curriculum or lack of guidance on AI in medical education from medical education governing bodies. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should have an understanding in the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues and acquiring knowledge about data appropriate to AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.
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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.