ArticleOral diseases2026
Can Large Artificial Intelligence-Based Linguistic Models Help to Obtain Information About Burning Mouth Syndrome?
Article in Oral diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Applications of generative artificial intelligence in chronic pain: a scoping review.Journal of anesthesia · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBurning Mouth Syndrome (BMS) is an idiopathic chronic orofacial pain disorder with diagnostic and therapeutic challenges. Inexperienced clinicians may desperately resort to online information. The objective of this study was to evaluate the usefulness, quality, and readability of responses generated by three artificial intelligence large language models (AI-LLMs)-ChatGPT-4, Gemini, and Microsoft Copilot-to frequent questions about BMS. MATERIALS AND
methodsNine clinically relevant open-ended questions were identified through search-trend analysis and expert review. Standardized prompts were submitted, and responses were independently rated by 12 international experts using a 4-point usefulness scale. Quality was evaluated using the QAMAI tool. Readability was measured using Flesch-Kincaid Grade Level and Reading Ease scores. Statistical analyses included Kruskal-Wallis and Bonferroni correction.
resultsAll AI-LLMs produced moderately useful responses, with no significant difference in global performance. Gemini achieved highest overall quality scores, particularly in relevance, completeness, and source provision. Copilot scored lower in usefulness and source provision. No significant differences were obtained among AI-LLMs. Average readability corresponded to 12th grade, with ChatGPT requiring the highest proficiency.
conclusionsAI-LLMs show potential for generating reliable information on BMS, though variability in quality, readability, and source citation remains concerning. Continuous optimization is essential to ensure their clinical integration.
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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.