Accepted for/Published in: JMIR Medical Education
Date Submitted: Mar 6, 2019
Open Peer Review Period: Mar 7, 2019 - Mar 31, 2019
Date Accepted: Apr 16, 2019
(closed for review but you can still tweet)
Applications and Challenges of Implementing Artificial Intelligence in Medical and Healthcare Education: An Integrative Review
ABSTRACT
Background:
Since the advent of artificial intelligence (AI) since 1955, the applications of artificial intelligence (AI) have risen over the years. However, there has been little interest in AI in medical education until the last two decades, with an increase in the number of publications and citations on the field. Currently to our knowledge, limited articles are discussing or reviewing the current use of AI in medical education.
Objective:
This study aims to review the current applications of AI in medical education, as well as the challenges of implementing AI in medical education.
Methods:
Medline (Ovid), EBSCOhost ERIC & Education Source and Web of Science were searched with explicit inclusion and exclusion criteria. Selected articles were analyzed in full-text using the Extension of Technology Acceptance Model (ETAM) and the Diffusions of Innovations (DOI) theory. Data was subsequently pooled together and analyzed quantitatively.
Results:
A total of 34 articles were identified; three primary uses of AI in medical education were identified: 1 on curriculum review, 29 on learning support, and four on assessment of students' learning. Thirty-one articles described the challenges of implementation of AI in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education, and technical challenges while developing the AI system.
Conclusions:
The primary use of AI in medical education was on learning support due to mainly its ability to provide individualized feedback. Little emphasis is placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations respectively. Methodological improvements are required to increase the adoption of AI by addressing the technical difficulties of creating an AI system and using novel methods to assess the effectiveness of AI.
Citation
Per the author's request the PDF is not available.
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.