ArticleJMIR medical education2024
Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes.BMC medical education · 2026Pooled it
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Evaluating ChatGPT-4o as an Educational Support Tool for the Emergency Management of Dental Trauma: Randomized Controlled Study Among Students.JMIR medical education · 2025Trial
- Large language models answering structured questions on external cervical resorption: a comparative study.Odontology · 2026Article
- Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians.JMIR medical education · 2026Article
- DentalEdu-AI graph: mapping evidence-grounded and knowledge-structured AI for dental education, assessment, and accuracy.The Saudi dental journal · 2026Article
- Prompt Framing and Evidence Requirement for AI-Generated Educational Responses in Dental Education: Experimental Study.JMIR medical education · 2026Article
- Comparison of Human and Artificial Intelligence (AI) in Writing and Rating Restorative Dentistry Essays.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- Article
- Reproducibility of Mandibular Cortical Index Classification Among Dental Examiners and a Single Generative AI Platform: An Observer Agreement Study.Clinics and practice · 2026Article
- Artificial Intelligence Integration in U.S. Healthcare Professional Degree Programs: A Rapid Scoping Review.Research square · 2026Article
- Referral patterns to a Middle Eastern oral medicine service: a retrospective analysis.BMC health services research · 2026Article
- Integrating AI and Into Dental Hygiene Documentation Education.Journal of dental education · 2026Article
- The landscape of virtual simulation in undergraduate dental education with implications for artificial intelligence incorporation.NPJ digital medicine · 2026Article
- Performance of large language models in endodontics: accuracy, consistency, and benchmarking with consensus guidelines.BMC oral health · 2026Article
- AI-Powered Clinical Decision Support in Dentistry: Comparative Evaluation of Large Language Models for Oral Medicine and Periodontal Diagnosis.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Implementation of an artificial intelligence-powered training platform in oral medicine and pathology education: a comparative study.BMC medical education · 2026Article
- Calibration of AI large language models with human subject matter experts for grading of clinical short-answer responses in dental education.BMC oral health · 2026Article
- Evaluation of the performance of four different large language models (ChatGPT, DeepSeek, Copilot, and Gemini) in answering oral, and maxillofacial radiology questions: pilot study.BMC oral health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.