## 4 Discussion
### Guideline Adherence and Clinical Consistency in Chatbot Responses  
In the present study, the 2020 IADT guideline [21] served as the reference standard for evaluating both chatbot-generated responses and expert assessments. Notably, some chatbots failed to provide answers consistent with this guideline or appeared to rely on outdated versions of earlier recommendations. Such discrepancies are not merely technical oversights but raise serious concerns regarding the trustworthiness of AI-generated clinical content. For example, in response to questions about radiographic indications, only ChatGPT-4o correctly referenced the most up-to-date principle--ALADAIP. In contrast, Claude 3.7 and Gemini Advanced cited the outdated \"As Low As Reasonably Achievable\" (ALARA) principle, which has since been replaced in pediatric dental radiology contexts. DeepSeek emphasized minimizing radiation exposure but did not reference any specific guideline. Furthermore, although the current IADT guidelines advise that CBCT should be reserved for well-justified clinical indications [21], only Gemini Advanced and Claude 3.7 accurately acknowledged this restriction. The remaining models omitted CBCT-specific recommendations in their responses. These inconsistencies highlight a critical limitation in LLM-based tools: the risk of disseminating outdated or inaccurate clinical recommendations. Such limitations may undermine clinician and patient trust--especially in pediatric care settings where strict adherence to current guidelines is essential for ensuring both safety and efficacy. In response to the question regarding the clinical management of intruded or laterally luxated primary teeth with root displacement toward the developing permanent tooth germ, ChatGPT-4o and Gemini Advanced provided recommendations consistent with the current IADT guidelines. In contrast, DeepSeek and Claude 3.7 offered outdated responses that did not align with contemporary clinical standards. When asked about the expected timeframe for spontaneous repositioning of an intruded primary tooth, Claude 3.7 provided the most guideline-consistent answer. However, regarding clinical management, both ChatGPT-4o and DeepSeek incorrectly recommended extraction when the root was directed toward the permanent successor--contradicting the IADT's recommendation of an observation-based approach. Conversely, Claude 3.7 and Gemini Advanced adhered more closely to current guidelines by recommending clinical monitoring and reserving extraction for cases with clear indications. For lateral luxation injuries, ChatGPT-4o accurately indicated that splinting is recommended in cases of severe displacement and correctly specified the appropriate duration, in accordance with the IADT guidelines. DeepSeek stated that splinting is generally not advised in primary teeth but noted that it may be considered in cases of excessive mobility. Gemini Advanced and Claude 3.7, however, did not mention splinting or provide any guidance regarding its duration. Regarding questions assessing the recommended follow-up intervals for various types of TDIs, all chatbots exhibited varying degrees of inconsistency with the current IADT guidelines. However, when asked about the appropriate clinical response in cases where there is a discrepancy between the patient's history and clinical findings, all chatbots correctly identified the potential for child abuse and emphasized the importance of mandatory reporting in suspected cases. This demonstrates that the models showed sensitivity to child protection issues and adhered to the ethical and legal principles outlined in the IADT guidelines--highlighting the potential of AI technologies to integrate ethical awareness. In contrast, the clinical reliability of these models remains limited when it comes to more nuanced recommendations. For example, only ChatGPT-4o correctly indicated that splinting is required in root fractures involving coronal fragment displacement and specified the appropriate duration.