ArticleFrontiers in oral health2025
Enhancing patient-centered information on implant dentistry through prompt engineering: a comparison of four large language models.
Article in Frontiers in oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diagnostic accuracy of large language models in the classification of superior labial frenulum attachments.Odontology · 2026Article
- A cross-sectional evaluation of Large Language Model answers to dental questions.BMC oral health · 2026Article
- Large language models in implant dentistry: a scoping review of applications, performance, and limitations.BMC oral health · 2026Article
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Evaluating the effectiveness of an artificial intelligence-based chatbot for reducing dental anxiety among adult dental patients.Frontiers in oral health · 2026Article
- Artificial intelligence applications in automated dental report generation - a scoping review.Frontiers in oral health · 2026Review
- Evaluating the performance of AI chatbots in responding to dental implant FAQs: A comparative study.BMC oral health · 2025Article
- The evaluation of tooth whitening from a perspective of artificial intelligence: a comparative analytical study.Frontiers in digital health · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
Background: Patients frequently seek dental information online, and generative pre-trained transformers (GPTs) may be a valuable resource. However, the quality of responses based on varying prompt designs has not been evaluated. As dental implant treatment is widely performed, this study aimed to investigate the influence of prompt design on GPT performance in answering commonly asked questions related to dental implants. Materials and methods: Thirty commonly asked questions about implant dentistry - covering patient selection, associated risks, peri-implant disease symptoms, treatment for missing teeth, prevention, and prognosis - were posed to four different GPT models with different prompt designs. Responses were recorded and independently appraised by two periodontists across six quality domains. Results: All models performed well, with responses classified as good quality. The contextualized model performed worse on treatment-related questions (21.5 ± 3.4, Conclusion: GPTs can provide accurate, complete, and useful information for questions related to dental implants. While prompt designs can enhance response quality, further refinement is necessary to optimize its performance.
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