Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models (Preprint)

JMIR Medical Education, Год журнала: 2024, Номер 10, С. e52346 - e52346

Опубликована: Июнь 19, 2024

Abstract Instructional and clinical technologies have been transforming dental education. With the emergence of artificial intelligence (AI), opportunities using AI in education has increased. recent advancement generative AI, large language models (LLMs) foundation gained attention with their capabilities natural understanding generation as well combining multiple types data, such text, images, audio. A common example ChatGPT, which is based on a powerful LLM—the GPT model. This paper discusses potential benefits challenges incorporating LLMs education, focusing periodontal charting use case to outline LLMs. can provide personalized feedback, generate scenarios, create educational content contribute quality However, challenges, limitations, risks exist, including bias inaccuracy created, privacy security concerns, risk overreliance. guidance oversight, by effectively ethically integrating LLMs, incorporate engaging learning experiences for students toward readiness real-life practice.

Язык: Английский

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Performance of Four AI Chatbots in Answering Endodontic Questions

Journal of Endodontics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Large Language Models in peri-implant disease: How well do they perform?

Journal of Prosthetic Dentistry, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student assessments

Clinical Oral Investigations, Год журнала: 2024, Номер 28(11)

Опубликована: Окт. 7, 2024

Abstract Objectives The advent of artificial intelligence (AI) and large language model (LLM)-based AI applications (LLMAs) has tremendous implications for our society. This study analyzed the performance LLMAs on solving restorative dentistry endodontics (RDE) student assessment questions. Materials methods 151 questions from a RDE question pool were prepared prompting using OpenAI (ChatGPT-3.5,-4.0 -4.0o) Google (Gemini 1.0). Multiple-choice sorted into four subcategories, entered answers recorded analysis. P-value chi-square statistical analyses performed Python 3.9.16. Results total answer accuracy ChatGPT-4.0o was highest, followed by ChatGPT-4.0, Gemini 1.0 ChatGPT-3.5 (72%, 62%, 44% 25%, respectively) with significant differences between all except GPT-4.0 models. subcategories direct restorations caries indirect endodontics. Conclusions Overall, there are among LLMAs. Only ChatGPT-4 models achieved success ratio that could be used caution to support dental academic curriculum. Clinical relevance While clinicians field-related questions, this capacity depends strongly employed model. most performant acceptable rates in some subject sub-categories analyzed.

Язык: Английский

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Large language models in periodontology: Assessing their performance in clinically relevant questions

Journal of Prosthetic Dentistry, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

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Improving the health literacy of persons with epilepsy

Epilepsy & Behavior, Год журнала: 2024, Номер 163, С. 110237 - 110237

Опубликована: Дек. 31, 2024

Язык: Английский

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Correlates of Medical and Allied Health Students’ Engagement with Generative AI in Nigeria

Medical Science Educator, Год журнала: 2024, Номер 35(1), С. 269 - 280

Опубликована: Сен. 30, 2024

Язык: Английский

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