## 1 Introduction  
Artificial intelligence (AI) encompasses a wide range of technologies that enable computer systems to perform tasks traditionally associated with human intelligence--such as learning, reasoning, interpretation, and data analysis--without direct human intervention [1]. A subset of AI, machine learning (ML), refers to systems that can identify patterns and learn from data without relying solely on predefined algorithms or manual input [2]. Recent advances in AI and ML have significantly contributed to fields such as natural language processing (NLP) and deep learning, leading to the development of models capable of analyzing and interpreting complex textual information. These models have demonstrated substantial potential in healthcare applications, including responding to medical inquiries, assisting in diagnoses, and supporting treatment planning [3]. Chat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI and released in November 2022, is based on the GPT-4 architecture and is considered one of the most advanced autoregressive language models to date [4; 5; 6]. Similarly, Google introduced its AI chatbot Bard in 2023, followed by the release of Gemini in 2024--an advanced multimodal platform capable of processing complex data types, including visual and graphical inputs [7; 8]. Developed in China, the AI model DeepSeek-R1 garnered global attention following the launch of its chatbot in December 2024. The model is distinguished by its focus on transparency, reproducibility, accessibility, and affordability, which have contributed to its rapid and widespread adoption worldwide [9; 10]. In the same year, Anthropic released Claude 3.7 Sonnet, a model designed to achieve high performance in language comprehension and multimodal data analysis [11; 12]. Traumatic dental injuries (TDIs) are highly prevalent among children and young adults, accounting for approximately 5% of all bodily injuries. One in four school-aged children experiences at least one dental trauma, while nearly one-third of adults report a history of trauma to their permanent teeth--most of which occur before the age of 19 [13]. In early childhood, falls and collisions are common due to the ongoing development of motor control and coordination. These everyday incidents frequently result in injuries to the primary dentition. However, not all oral injuries are accidental; some may be caused by adverse events such as child abuse, motor vehicle accidents, or other external causes [14]. Mismanagement of TDIs often results from inadequate initial clinical assessment. Prompt intervention and appropriate treatment are crucial for improving the prognosis of traumatized teeth [15]. Accurate diagnosis and timely management of acute TDIs are essential for ensuring the long-term survival of affected teeth and for supporting the normal development of the dentoalveolar complex [16]. Successful treatment outcomes depend not only on the clinician's expertise but also on how promptly the injury is recognized and managed at the time of occurrence. Despite the critical importance of timely decision-making, diagnostic and management errors remain common in TDI cases, especially when providers lack immediate access to guidelines or expert consultation. In this context, AI tools--particularly large language models (LLMs)--have emerged as potential clinical support systems in dental traumatology [17; 18]. In pediatric dentistry, however, the number of AI validation studies remains scarce. While a few recent papers have explored LLMs' performance in general dental trauma scenarios [15; 16; 19; 20], there is a notable gap in research specifically targeting TDIs in the primary dentition. Given the anatomical and clinical differences between primary and permanent teeth--as well as the specific decision-making nuances involved--this area warrants further investigation.