ArticleFrontiers in digital health2025
The evaluation of tooth whitening from a perspective of artificial intelligence: a comparative analytical study.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed.
- Accuracy and Temporal Consistency of ChatGPT and Gemini in Responding to Textbook and Patient-Oriented Dental Bleaching Questions: A Multi-Session Comparative Study.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- Performance of AI chatbots in answering questions on tooth bleaching: a multilingual comparative study.BMC oral health · 2026Article
- Feasibility of a multi-metric framework for evaluating patient-facing AI communication in cosmetic dentistry: an exploratory proof-of-concept study.Frontiers in oral health · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) chatbots are increasingly consulted for dental aesthetics information. This study evaluated the performance of multiple large language models (LLMs) in answering patient questions about tooth whitening. Methods: 109 patient-derived questions, categorized into five clinical domains, were submitted to four LLMs: ChatGPT-4o, Google Gemini, DeepSeek R1, and DentalGPT. Two calibrated specialists evaluated responses for usefulness, quality (Global Quality Scale), reliability (CLEAR tool), and readability (Flesch-Kincaid Reading Ease, SMOG index). Results: The models generated consistently high-quality information. Most responses (68%) were "very useful" (mean score: 1.24 ± 0.3). Quality (mean GQS: 3.9 ± 2.0) and reliability (mean CLEAR: 22.5 ± 2.4) were high, with no significant differences between models or domains ( Conclusions: Contemporary LLMs provide useful and reliable information on tooth whitening but deliver it at a reading level incompatible with average patient health literacy. To be effective patient education adjuncts, future AI development must prioritize readability simplification alongside informational accuracy.
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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.