Accepted for/Published in: JMIR Medical Education
Date Submitted: Sep 5, 2023
Date Accepted: Jun 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Warning: This is an author submission that is not peer-reviewed or edited.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.
Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models
ABSTRACT
Instructional and clinical technologies have been transforming dental education. With the emergence of artificial intelligence (AI), the opportunities of utilizing AI in education has increased. With the recent advancement of generative AI, Large Language Models (LLMs) gained attention with their capabilities in natural language understanding and generation. A common example has been ChatGPT, which is based on a powerful LLM, generative pretrained transformer (GPT) model. This article discusses the potential benefits and challenges of incorporating LLMs in dental education, focusing on periodontal charting with a use case to outline capabilities of LLMs. LLMs can provide personalized feedback, generate case scenarios, and create educational content to contribute to the quality of dental education. However, challenges, limitations and risks exist, including bias and inaccuracy in the content created, privacy and security concerns, and the risk of overreliance. With the guidance and oversight, and by effectively and ethically integrating LLMs, dental education can incorporate engaging and personalized learning experiences for students towards readiness for real-life clinical practice.
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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.