## 1 Introduction  
The recent development of large language models (LLMs) has sparked widespread interest owing to their potential impact across many disciplines. These models, powered by massive transformer-based architectures and trained on vast datasets of text and code, have demonstrated remarkable capabilities in natural language processing (NLG). A prime example is the language model ChatGPT, which possesses an extensive training dataset comprising websites, books, and the entirety of Wikipedia [1; 2]. This allows ChatGPT to perform a wide range of tasks, from question answering to creative writing [1]. Rapid progress in LLMs has generated significant interest in their potential applications across various fields. From revolutionizing natural language processing to automating content creation and powering knowledge-intensive applications, LLMs are poised to significantly impact diverse industries [2]. The medical field, in particular, presents fertile ground for LLM integration. Patients, caregivers, healthcare providers, researchers, academics, and legal professionals all benefit from these technologies. The emerging literature suggests that large language models (LLMs) demonstrate potential for various applications within the healthcare domain, including triage, medical information translation, clinical research support, workflow optimization, medical education, and patient consultations. However, their current capabilities and effectiveness in clinical settings remain under investigation. Further studies are needed to validate their practical utility and limitations [2]. Preliminary research indicates that ChatGPT has the potential to be applied in healthcare settings, such as guiding patients to relevant departments, facilitating communication across language barriers, and supporting scientific writing through literature-review assistance and article summarization [3; 4; 5]. LLMs also have the potential for clinical applications,ranging from diagnostic support and treatment recommendations to generating discharge summaries and medical notes and even extracting structured data from these documents [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Beyond clinical care, LLMs can be utilized for effective patient education [18; 19; 20]. Beyond clinical care, LLMs can be utilized for effective patient education [21; 22; 23]. LLMs can also be valuable assets in research projects, such as systematic reviews, where they can aid in tasks such as preparing Boolean search terms, screening abstracts, classifying articles, and performing content analysis [24; 25]. While research suggests promising potential for LLMs in extracting explicitly stated information from articles, their performance appears less satisfactory for tasks requiring deeper analysis [24; 26; 27]. Numerous studies in orthodontics have analyzed the information provided by LLMs, particularly ChatGPT, on orthodontic topics [19; 20; 27; 28; 29; 30]. Findings regarding the accuracy of ChatGPT responses vary. While some studies utilizing earlier versions found unreliable responses, others using updated models reported a higher accuracy. Recent research has also assessed the ability of ChatGPT 3.5 and 4.0 to generate PICO-based queries in orthodontics, suggesting their effectiveness in producing research questions, keywords, and Boolean queries tailored to specific research objectives under appropriate settings [31]. Furthermore, a recent study compared ChatGPT's proficiency in assessing the reporting quality of randomized controlled trial abstracts according to the CONSORT-A statement against human reviewers. This study suggests LLMs like ChatGPT can potentially automate medical literature appraisal, aiding in the identification of accurately reported research [32]. Although LLMs hold significant promise, it is important to recognize that their performance across different applications can be inconsistent. LLM performance can vary from falling below human standards to achieving results comparable to human capabilities [25; 26; 31; 33].