## 4 Discussion
### Strengths, Limitations, and Methodological Challenges  
Unlike earlier studies that focused either on general dental trauma or solely on ChatGPT models, the present work offers a broader and clinically specific comparison by targeting primary dentition and incorporating less-explored LLMs. Methodologically, the study adopted a human-centered and balanced approach. Five distinct readability indices (FRES, FKGL, GFI, SMOG, and CLI) were used to enhance objectivity. Each question was submitted to all four models in two separate sessions, enabling assessment of response consistency over time. The questions and scoring criteria were grounded in the most recent, evidence-based IADT guidelines. Although the question set was standardized and guideline-based, we acknowledge that modifying the number or scope of items--such as adding or replacing questions--could affect performance scores. However, the overall trends in model behavior, particularly regarding accuracy and guideline adherence, are expected to remain robust due to the systematic design and expert validation of the original set. The inclusion of response time and readability further allowed for a truly multidimensional evaluation. Nevertheless, this study has several limitations. First, it assessed only four general-purpose AI models, and their performance may vary in other clinical contexts. Second, the exclusion of domain-specific models like Med-PaLM or BioGPT--due to their restricted availability--may limit the generalizability of the findings to highly specialized or clinical-only scenarios. However, this study focused on evaluating publicly accessible, general-purpose models that are more commonly used by non-specialist clinicians or caregivers in real-world pediatric dental trauma situations. Third, the exclusive use of English-language inputs and outputs may limit the generalizability of our findings. While English is widely used in international research and forms a core part of LLM training corpora, pediatric dental practice often involves multilingual interactions with patients and caregivers. LLMs may exhibit variable performance across different languages due to disparities in training data volume, grammar structure, and semantic complexity. Future investigations should explore the multilingual robustness of these models to ensure broader applicability in global health contexts. Lastly, given the rapid pace of AI development, these findings represent a snapshot in time and may not reflect future model updates or improvements. In addition to the limitations acknowledged, this study faced several practical and methodological challenges. First, aligning the evaluation criteria across diverse AI models proved difficult--especially given differences in language generation patterns and interface design [43]. Second, although the questions were based on standardized guidelines, LLMs sometimes interpreted open-ended prompts inconsistently, introducing variability not solely attributable to model quality [44]. This issue has also been reported in similar chatbot assessment studies [15; 19]. Third, readability assessment--though comprehensive in our design--relied on linguistic indices that may not fully capture real-world comprehension, a limitation noted by prior reviews [45; 46]. Finally, balancing objectivity and clinical relevance in expert scoring posed a challenge, as subjective interpretation of nuanced responses could vary slightly among evaluators. These challenges underscore the complexity of evaluating AI-generated medical content and the need for refined, standardized frameworks in future studies. To mitigate the risks associated with overreliance on LLMs in pediatric dental settings, clinicians should adopt a cautious and structured approach. LLM-generated responses should never replace clinical expertise but may serve as adjunctive tools for triage or patient education when verified against trusted sources.