## 4 Discussion
### Comparison with Previous Studies
#### 4.1.5 Potential Determinants of Performance Differences  
The observed differences in chatbot performance likely reflect variations in model architecture, training datasets, and update schedules. For instance, ChatGPT-4o and Claude 3.7 are known to leverage advanced multi-modal and retrieval-augmented architectures, which may enhance their contextual coherence and factual accuracy [38]. Gemini Advanced, while optimized for general performance, may exhibit weaker alignment with domain-specific medical guidelines due to training corpus composition [39]. Similarly, DeepSeek's lower readability and response latency might stem from model scale, decoding strategies, or Application Programming Interface throttling behaviors [40]. These architectural and training-related nuances--although proprietary--are essential considerationswhen interpreting benchmark results, as they suggest that output quality is not solely a function of question complexity, but also of design decisions embedded in each LLM's development pipeline.  
Although our study utilized two independent sessions to assess response consistency, it is important to acknowledge that chatbot outputs may still be subject to temporal variability. Factors such as stochastic decoding (e.g., temperature and top-k sampling), silent Application Programming Interface updates, and evolving model versions can lead to variability in repeated queries--even when prompts are identical. This phenomenon, often referred to as \"model drift,\" complicates the reproducibility of chatbot-based studies and suggests that future research should include time-stamped interactions, model versioning, and possibly prompt seeding for standardization. While we attempted to minimize variability by spacing sessions within a short timeframe and avoiding known model update periods, these uncontrollable dynamics remain a key limitation in LLM benchmarking.  
The observed variability in LLM outputs--particularly when responding to identical prompts across sessions--may reflect broader challenges in dynamic decision-making and internal calibration mechanisms intrinsic to large neural models. These systems, like multi-layer neural networks used in physical robotics, learn complex non-linear relationships and evolve based on training data, update frequency, and operational context. Similar to how model-free approaches can capture dynamic tool behaviors in robotics without pre-defined physical equations [41], LLMs attempt to align their responses with linguistic, semantic, and clinical patterns in a non-deterministic fashion. Incorporating insights from such calibration frameworks may enrich our understanding of how LLMs adapt--or fail to adapt--to structured guideline-based scenarios in healthcare.  
Although this study evaluated LLM outputs solely based on textual prompts and responses, the future integration of multimodal clinical data--including radiographs, intraoral images, and patient histories--represents a promising direction for enhancing AI-driven decision support in pediatric dentistry. In robotics and sensor-rich environments, models that integrate heterogeneous inputs, such as recurrent neural networks for gesture recognition [42], have demonstrated improved contextual sensitivity and responsiveness. Analogously, LLMs capable of harmonizing multiple data streams could facilitate more accurate and clinically aligned responses in complex diagnostic or triage scenarios.