## 4 Discussion
### LLMs That Have Been Applied for Generating Answers to Questions Related to Orthodontics  
Most of the reported studies have utilized ChatGPT-3.5 and ChatGPT-4 [25, 26, 27, 28, 29, 30, 31, 34], Google's Bard/Gemini [27, 31, 34], and Microsoft's Bing/Copilot AI [30, 31]. The most commonly used was ChatGPT, which is a LLM, which is rooted in a natural language processing [NLP], and enables computer systems to comprehend the natural language inputs, which includes techniques like machine learning [11]. These LLMs are developed using neural networks and undergo training using large amounts of text datasets from various sources. Once trained, their task is to generate coherent, human-like conversational responses to queries/questions/prompts based on the data that they have been trained on. This process is accomplished through the deep-learning algorithms and advanced modeling [11, 12, 13]. Another LLM known as Google Bard, now known as Gemini, was recently introduced by Google and has been made available for public access. A key difference between Gemini and ChatGPT is that Gemini utilizes real-time internet to extract the most updated information while generating the responses to the queries [35]. Another widely recognized LLM, known as Bing Chat, which has been re-launched as Copilot, was introduced by Microsoft. This model effectively addresses the limitations encountered by ChatGPT, including the ability to update in accordance with current events through internet access [36]. Methods That Have Been Adopted for Assessing the Level of Accuracy and Reliability of the Responses Generated for Questions Related to Orthodontics  
Most of the reported studies have employed Likert scales to evaluate the accuracy of the responses, which were assessed by experienced specialists [25, 26, 28, 30, 34]. The most commonly used tool for assessing the reliability of the responses was the DISCERN tool. This tool is a validated and a reliable tool developed for healthcare professionals and patients to evaluate the reliability of health-related texts [37, 38, 39, 40]. It comprises 16 questions, where the first 8 questions are related to the reliability and next 7 questions are for assessing the treatment options, with the last question assessing the overall quality of the article. These questions are rated on 5-point scale, with 1 indicating low quality and 5 indicating high quality [37]. Figure 2: Forest plot analysis for ChatGPT-4 with that of other LLMs [28, 29, 30, 31, 34]. The quality and the usefulness of the responses generated by the LLMs were assessed using Global Quality Scale (GQS). The quality of information was evaluated on a five-point Likert scale. A score of 1 is considered as poor quality, a score of 2 is considered as generally poor quality, a score of 3 is considered as moderate quality, a score of 4 is considered as good quality, and a score of 5 is considered as excellent quality. This scale is designed to assess the quality of information, the flow, and the convenience of use of the information available online [41; 42; 43]. In one study, the quality of responses generated by the LLMs was assessed using the AOI index. This index measures the quality and completeness of the responses generated by the LLMs on a 10-point visual analog scale. It comprises five domains: factual accuracy, corroboration, consistency, clarity and specificity, and relevance of response. A higher score suggests that the responses are more accurate and complete [44]. The most common tool used for assessing the reliability of the readability responses generated by the LLMs was the Flesch Reading Ease Score (FRES). This is a valid tool for evaluating the readability and grading the comprehension of the generated text. The readability was measured using the Flesch Reading Ease Score calculator. The scoring system ranges from 0 to 100, based on which the readability is determined as easy or difficult [45; 46].