Artificial Intelligence in Early Childhood Caries: Accuracy and Temporal Consistency Performance of Large Language Model–Based Chatbots

Abstract

Objective: To evaluate the accuracy of responses provided by large language model (LLM)-based next-generation generative artificial intelligence (AI) tools regarding early childhood caries (ECC) and to examine their performance in terms of temporal consistency.Method: Twenty-one true/false questions on ECC, developed in accordance with the policy of the American Academy of Pediatric Dentistry (AAPD), were posed over three days to five chatbot systems, including both publicly accessible and subscription-based platforms: ChatGPT version 5.2 (OpenAI), DeepSeek (DeepSeek AI), Perplexity (Perplexity AI), Gemini (Google), and Copilot (Bing/Microsoft). The responses were recorded and compared with the correct answers. Results: All chatbots demonstrated similar accuracy performance (p>0.05). Chatbots’ performance varied according to the day and time (p