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This study benchmarked ChatGPT-5, Claude AI (Sonnet 4.0), and Perplexity (Mistral Large 2) on their ability to answer dental trauma questions, assessing accuracy, consistency, readability, and information quality. Perplexity demonstrated the highest accuracy on true/false questions, while ChatGPT excelled in readability, Perplexity in understandability and actionability, and Claude in information reliability for open-ended questions. The results suggest that LLM-based chatbots can play a complementary role in dental trauma management, with tool selection dependent on the specific application.
Despite achieving acceptable global quality scores, LLM-based chatbots exhibit variable performance across accuracy, consistency, and information quality when applied to dental trauma management, highlighting the need for careful tool selection and human oversight.
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.