Previously submitted to: JMIR Mental Health (no longer under consideration since Oct 19, 2024)
Date Submitted: Oct 19, 2024
Warning: This is an author submission that is not peer-reviewed or edited.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.
The role of generative artificial intelligence in psychiatric education: a scooping review
ABSTRACT
Background:
The increasing prevalence of mental health conditions, exacerbated by the COVID-19 pandemic, underscores the urgent need for improved psychiatric education. This study investigates the potential role of generative artificial intelligence (GenAI) in psychiatric education.
Objective:
While GenAI has shown promising outcomes in medical education, its application in psychiatric training remains underexplored. In this study, we hope to highlight the potential roles of GenAI in psychiatric education.
Methods:
We conducted a scoping review to identify the role of GenAI in psychiatric education based on the educational framework of the Canadian Medical Education Directives for Specialists (CanMEDS).
Results:
Of the 6412 papers identified, 5 studies met the inclusion criteria, revealing key roles for GenAI in case-based learning, simulation, content synthesis, and assessments. Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted.
Conclusions:
Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted. This study contributes to the understanding of how GenAI can enhance psychiatric education and suggests future research directions to refine its use in training medical students and primary care physicians. GenAI holds significant potential to address the growing demand for mental health professionals, provided its limitations are carefully managed.
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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.