ArticleAustralian endodontic journal : the journal of the Australian Society of Endodontology Inc2026
Evaluation of Large Language Model-Based Chatbots for Dental Trauma Management: A Comparative Study Based on Accuracy, Consistency and Information Quality.
Article in Australian endodontic journal : the journal of the Australian Society of Endodontology Inc, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study evaluated and compared the accuracy, consistency, readability, and information quality of three LLM-based chatbots, namely ChatGPT-5, Claude AI (Sonnet 4.0), and Perplexity (Mistral Large 2), in addressing traumatic dental injury questions. Forty true/false statements were submitted to each chatbot three times at weekly intervals to assess accuracy and consistency. Additionally, chatbot responses for 25 open-ended case-based questions were evaluated for readability, understandability and actionability, information reliability and quality. For true/false questions, Perplexity showed the highest accuracy, followed by Claude and ChatGPT. For open-ended responses, ChatGPT excelled in readability (FRE: 62.4 ± 7.6), Perplexity in understandability (91.0 ± 4.3) and actionability (93.0 ± 6.4) and Claude in information reliability (mDISCERN total: 61.2; no variability observed). All chatbots achieved acceptable global quality scores (> 4.4). These findings emphasise the complementary role of chatbots in dental trauma management. Tool selection should be based on intended use, while continued human oversight remains essential in clinical decision-making.
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