ArticleDento maxillo facial radiology2024
How well do large language model-based chatbots perform in oral and maxillofacial radiology?
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 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
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Can Large Language Models Serve as Reliable Tools for Information in Dentistry? A Systematic Review.International dental journal · 2025Pooled it
- The role of large language models in dental diagnosis, decision-making, and communication: A systematic review.The Japanese dental science review · 2026Review
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.Oral radiology · 2026Article
- Article
- Comparative Evaluation of Large Language Models for Reporting Jaw Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Multidimensional Evaluation of AI-Generated Patient Information on Cone Beam-Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Comparative assessment of quality, consistency, and reference accuracy of MIH-related clinical information generated by ChatGPT-4o and DeepSeek R1.BMC oral health · 2026Article
- A comparative evaluation of two large language models in pediatric dentistry.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
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Evaluating chat generative pre-trained transformer responses to common patient questions on temporomandibular disorders.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Educational aspects of artificial intelligence in oral and maxillofacial radiology: insights from a scoping review.BMC medical education · 2025Article
- Performance of ChatGPT-4o, Gemini Advanced-2.0, and DeepSeek-V3 in the diagnosis and management of temporomandibular disorders.BMC oral health · 2025Article
- Comparison of chatbots' accuracy in endodontics questions in dentistry specialization exam in Türkiye: ChatGPT-4o, Gemini Advanced, Copilot, and Claude.BMC oral health · 2025Article
- A Scoping Review of Large Language Models in Dental Education: Applications, Challenges, and Prospects.International dental journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesThis study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination.
methodsChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation. They were also classified as multiple-choice questions (MCQs) and short-answer questions (SAQs). The accuracy rates of the chatbots were compared with the performance of students, and further analysis was conducted based on the educational content and question type.
resultsThe students' overall accuracy rate was 81.2%, while that of the chatbots varied: 50.0% for ChatGPT, 65.4% for ChatGPT Plus, 50.0% for Bard, and 63.5% for Bing Chat. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0%. All chatbots scored less than 60.0% on MCQs, but performed better on SAQs.
conclusionsThe performance of chatbots in oral and maxillofacial radiology was unsatisfactory. Further training using specific, relevant data derived solely from reliable sources is required. Additionally, the validity of these chatbots' responses must be meticulously verified.
Indexed as
Identifiers
What OpenQuestion holds
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.