ArticleBMC oral health2026
Comparative evaluation of AI-driven large language models for periodontal-orthodontic patient questions: accuracy, comprehensiveness, readability, and temporal consistency.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI)-driven large language models (LLMs) are increasingly used to provide patient-oriented oral health information; however, their performance in answering periodontal-orthodontic patient questions remains unclear. This study aimed to compare the performance of contemporary AI-driven LLMs in answering periodontal-orthodontic patient questions by assessing response accuracy, comprehensiveness, readability, and temporal consistency.
methodsThirty patient-oriented periodontal-orthodontic questions were submitted to six contemporary LLMs (ChatGPT 5.5, Claude Sonnet 4.6, DeepSeek 3.2, Gemini 3.1 Pro, Grok 4.20, and PerioGPT) under standardized conditions, and the generated responses were analyzed. Accuracy and comprehensiveness were evaluated using a modified five-point Likert scale, whereas readability was assessed using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) indices. Temporal consistency was assessed by repeating the response-generation process one week later. Inter-model comparisons and temporal consistency analyses were performed using repeated-measures ANOVA or Friedman tests with Bonferroni-adjusted post hoc comparisons, as appropriate.
resultsSignificant differences were observed among the evaluated LLMs in accuracy (p < 0.001, Kendall's W = 0.401), comprehensiveness (p < 0.001, Kendall's W = 0.575), FRE (p < 0.001, partial η
conclusionsWhile PerioGPT and Gemini achieved the highest accuracy and comprehensiveness scores, and Claude, DeepSeek, and Grok produced more readable responses, the evaluated models generally demonstrated similar temporal consistency over time; however, no model consistently outperformed the others across all evaluated criteria. Therefore, AI-driven LLMs may support patient education and oral health literacy in periodontal-orthodontic care; however, their use as complementary tools under expert supervision is recommended.
Indexed as
Identifiers
42888393What Socratic 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 Socratic graph.