ArticleTheScientificWorldJournal2026
Comparative Analysis of Generative AI Language Models in Orthodontics: Evidence-Based Insights Into Perplexity, iASK, and ChatGPT 4o Mini.
Article in TheScientificWorldJournal, 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.
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Who cites it
1 citing paper in PubMed.
- Comparative Analysis of Generative AI Language Models in Orthodontics: Evidence-Based Insights Into Perplexity, iASK, and ChatGPT 4o Mini.TheScientificWorldJournal · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study is aimed at evaluating and comparing the scientific reliability of three large language models (LLMs), Perplexity, iASK, and ChatGPT 4o mini, based on their responses to orthodontic-related queries. Materials and Methods: The three LLMs were prompted with 10 clinical orthodontic questions, and their responses were assessed independently by two evaluators using a structured scoring system (0-10). Statistical analyses, including Pearson and Spearman correlations, Cronbach's alpha, and Wilcoxon signed-rank test, were performed to determine interevaluator reliability and model performance differences. Results: Perplexity achieved the highest mean score (7.2), followed by iASK (5.4) and ChatGPT 4o mini (5.2). High consistency between evaluators was observed (Cronbach Conclusion: Perplexity demonstrated superior performance in orthodontic-related queries compared to ChatGPT 4o mini and iASK. The findings highlight the importance of evaluating AI models for clinical applicability and reliability.
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Registered trials
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