## 4 Discussion  
The increasing integration of LLMs, particularly ChatGPT, into the evaluation of reporting quality in orthodontic research highlights both the potential and limitations of AI in clinical settings. Our study aimed to assess the completeness of reporting in the abstracts of randomized controlled trials (RCTs) and systematic reviews using the CONSORT-A and PRISMA guidelines. The results indicated that while ChatGPT demonstrated a commendable ability to assess certain reporting elements, discrepancies remained when compared to human reviewers. The findings revealed that ChatGPT achieved perfect agreement with human raters on several key items, such as trial design and objectives for RCTs and the identification of systematic reviews and their objectives for systematic reviews. This suggests that LLMs can effectively recognize and evaluate fundamental aspects of research reporting, which is consistent with previous studies that have highlighted the utility of AI in healthcare settings for tasks such as triage and diagnostic support [1,3]. However, the lower alignment of critical items, such as randomization details and eligibility criteria, highlights the limitations of LLMs in understanding delicate reporting requirements. Several factors may contribute to the observed discrepancies between ChatGPT and human reviewers when assessing reporting quality. ChatGPT's training data likely lack sufficient examples of high-quality, detailed reporting in orthodontic research, leading to gaps in its understanding of field-specific requirements. While the AI possesses broad knowledge, it may lack the deep, specialized understanding that human experts in orthodontics have, particularly regarding methodological nuances. LLMs may struggle to grasp the full context of a study, including the rationale behind certain methodological choices, which human experts can infer based on their experience and field knowledge. Additionally, reporting guidelines like CONSORT and PRISMA are periodically updated, and ChatGPT's training data might not reflect the most current versions, leading to evaluation discrepancies. Unlike human reviewers, ChatGPT cannot seek clarification or additional information when faced with ambiguous reporting, potentially resulting in misinterpretations. Moreover, discrepancies may stem from the inherent complexity of clinical trial designs and the subtleties involved in systematic reviews, which require a deep understanding of methodological precision that current LLMs may not fully grasp [2]. These factors collectively contribute to the AI's limitations in assessing complex aspects of research reporting in orthodontics. Moreover, the variability in performance across different reporting items underscores the need for the continued refinement of LLMs. While they can assist in automating the assessment process, reliance solely on these models can lead to oversights in critical reporting areas. This aligns with prior research indicating that LLMs, including ChatGPT, can perform comparably to human doctors in certain diagnostic scenarios but may struggle with more complex clinical decision making [4,11]. As the field of orthodontics continues to evolve, the integration of LLMs into research practices could enhance the efficiency of literature reviews and improve reporting standards. However, it is essential to approach this integration with caution to ensure that human expertise remains a cornerstone of the evaluation process. Future studies should focus on refining the prompts and training data used for LLMs, potentially incorporating feedback loops that allow for continuous learning and improvement [26]. Additionally, the potential of LLMs extends beyond evaluation; they could also facilitate improved reporting practices by guiding researchers in adhering to established guidelines, such as CONSORT and PRISMA. Automating parts of the literature review process can enhance the efficiency of research synthesis and promote higher standards of reporting [22,24].