## 4 Discussion
### Comparison with Previous Studies
#### 4.1.4 Readability  
Effective health communication necessitates presenting information in language that is accessible to individuals with varying levels of literacy. In the present study, the readability of responses generated by four AI chatbots was evaluated using five established indices. The findings indicated that, for all models, the readability scores exceeded the sixth-grade threshold recommended by the United States National Institutes of Health, suggesting limited accessibility for the general population. Among the evaluated chatbots, Claude 3.7 produced the most complex responses, characterized by the lowest FRES and highest FKGL scores--indicating a need for advanced academic literacy. In contrast, DeepSeek achieved the highest FRES and lowest SMOG Index scores, suggesting relatively higher accessibility. However, even DeepSeek's responses were classified as \"difficult,\" corresponding to a high school reading level. ChatGPT-4o and Gemini Advanced generated content requiring college-level literacy, while Claude 3.7's outputs demanded even more advanced reading skills. These findings are consistent with previous studies in various dental domains, where chatbot-generated content often required high literacy levels. For instance, research in oral cancer, endodontics, and orthodontics has shown that models such as ChatGPT-3.5, ChatGPT-4, and Claude frequently produce complex text that limits accessibility [35; 36; 37]. Similarly, a study evaluating the responses of ChatGPT-3.5, ChatGPT-4.0, and Google Gemini in responses to questions on TDIs reported that all models generated highly complex texts requiring at least a college-level reading ability [28]. Another investigation focusing specifically on TDIs found that responses from ChatGPT-3.5 and ChatGPT-4.0 produced responses understandable only to individuals with high school or higher education levels [16]. While Gemini has shown comparatively higher readability in some studies, its outputs have often been criticized for lacking informational depth and completeness. Notably, the present study is among the first to assess the readability of chatbot-generated content specific to TDIs in the primary dentition. Our analysis revealed no statistically significant correlation between readability and accuracy across the evaluated models. This finding suggests that a more readable response does not necessarily equate to a more clinically accurate one. One possible explanation is that simplifying language to improve readability may inadvertently lead to a loss of technical precision--especially in complex clinical contexts such as TDIs in the primary dentition. Conversely, highly accurate responses may incorporate specialized terminology and longer sentence structures, thereby reducing readability. This divergence underscores a key limitation in current LLMs: their inability to simultaneously optimize for clinical accuracy and linguistic accessibility. It also highlights the challenge of balancing these two dimensions in AI-generated patient education materials or clinical support tools, particularly when intended for non-specialist users such as caregivers or general practitioners. While readability scores provide valuable insight into the accessibility of chatbot-generated content, their interpretation must be contextualized within the intended use case. In our study, most chatbot responses did not meet the standard sixth- to eighth-grade readability thresholds commonly recommended for patient education materials. For instance, ChatGPT-4o and Claude 3.7 rarely produced responses below a FKGL of 8 or a SMOG Index below 9. However, this should not be viewed strictly as a limitation. The questions posed in this study were primarily technical in nature and designed to simulate real-world clinical decision-making scenarios in pediatric dental trauma. As such, they demanded comprehensive and nuanced responses that inherently elevate linguistic complexity.