ArticleThe Saudi dental journal2024
Assessing the quality of AI information from ChatGPT regarding oral surgery, preventive dentistry, and oral cancer: An exploration study.
Article in The Saudi dental journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- The role of large language models in dental diagnosis, decision-making, and communication: A systematic review.The Japanese dental science review · 2026Review
- Performance of ChatGPT in dental implant treatment planning: evaluation using the modified DISCERN, Global Quality Score, and accuracy-safety score.BMC oral health · 2026Article
- Comparative diagnostic accuracy of ChatGPT large language models and expert clinicians in complex oral and maxillofacial diseases.Scientific reports · 2026Article
- Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study.Frontiers in bioinformatics · 2026Article
- Awareness, usage, and perspectives on ChatGPT in dental education among graduate and undergraduate students.Scientific reports · 2025Article
- Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?Dental traumatology : official publication of International Association for Dental Traumatology · 2025Observational
- Evaluation of ChatGPT-4's performance on pediatric dentistry questions: accuracy and completeness analysis.BMC oral health · 2025Article
- Current Applications of Chatbots Powered by Large Language Models in Oral and Maxillofacial Surgery: A Systematic Review.Dentistry journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: Evaluation of the quality of dental information produced by the ChatGPT artificial intelligence language model within the context of oral surgery, preventive dentistry, and oral cancer. Methodology: This study adopted quantitative methods approach. The experts prepared 50 questions (including dimensions of, risk factors, preventive measures, diagnostic methods, and treatment options) that would be presented to ChatGPT, and its responses were rated for their accuracy, completeness, relevance, clarity or comprehensibility, and possible risks using a standardized rubric. To carry out the assessment of the responses by ChatGPT, a standardized scoring rubric was used. Evaluation process included feedback concerning the strengths, weaknesses, and potential areas of improvement in the responses provided by ChatGPT. Results: While achieving the highest score for preventive dentistry at 4.3/5 and being able to communicate the complex information coherently, the tool showed lower accuracy for oral surgery and oral cancer, scoring 3.9/5 and 3.6/5, respectively, with several gaps for post-operative instructions, personalized risk assessments, and specialized diagnostic methods. Potential risks, such as a lack of individualized advice, were shown in 53% of the oral cancer and in 40% of the oral surgery. While showing promise in some domains, ChatGPT had important limitations in specialized areas that require nuanced expertise. Conclusion: The findings point to the need for professional supervision while using AI-generated information and ongoing evaluation as capabilities evolve, for the assurance of responsible implementation in the best interest of patient care.
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Registered trials
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