Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models.

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES JMIR Medical Education Pub Date : 2024-09-27 DOI:10.2196/52346
Daniel Claman, Emre Sezgin
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Abstract

Unlabelled: Instructional and clinical technologies have been transforming dental education. With the emergence of artificial intelligence (AI), the opportunities of using AI in education has increased. With the recent advancement of generative AI, large language models (LLMs) and foundation models gained attention with their capabilities in natural language understanding and generation as well as combining multiple types of data, such as text, images, and audio. A common example has been ChatGPT, which is based on a powerful LLM-the GPT model. This paper 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 guidance and oversight, and by effectively and ethically integrating LLMs, dental education can incorporate engaging and personalized learning experiences for students toward readiness for real-life clinical practice.

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人工智能在口腔医学教育中的应用:大型语言模型和多模态基础模型的机遇与挑战。
无标签:教学和临床技术一直在改变着口腔医学教育。随着人工智能(AI)的出现,在教育中使用人工智能的机会也越来越多。随着最近生成式人工智能的发展,大型语言模型(LLM)和基础模型因其在自然语言理解和生成以及结合多种类型数据(如文本、图像和音频)方面的能力而备受关注。一个常见的例子是 ChatGPT,它基于一个强大的 LLM--GPT 模型。本文讨论了将 LLM 纳入口腔医学教育的潜在好处和挑战,重点是牙周病学制图,并通过一个使用案例来概述 LLM 的功能。LLMs 可以提供个性化反馈、生成病例情景并创建教育内容,从而提高口腔医学教育的质量。然而,挑战、限制和风险也是存在的,包括所创建内容的偏见和不准确性、隐私和安全问题以及过度依赖的风险。在指导和监督下,通过有效地、符合道德规范地整合 LLM,口腔医学教育可以为学生提供吸引人的、个性化的学习体验,为真实的临床实践做好准备。
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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
期刊最新文献
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