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AIM: This study aimed to assess the validity and reliability of AI chatbots, including Bing, ChatGPT 3.5, Google Gemini, and Claude AI, in addressing frequently asked questions (FAQs) related to dental trauma. METHODOLOGY: A set of 30 FAQs was initially formulated by collecting responses from four AI chatbots. A panel comprising expert endodontists and maxillofacial surgeons then refined these to a final selection of 20 questions. Each question was entered into each chatbot three times, generating a total of 240 responses. These responses were evaluated using the Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Any disagreements in scoring were resolved through evidence-based discussions. The validity of the responses was determined by categorizing them as valid or invalid based on two thresholds: a low threshold (scores of ≥ 4 for all three responses) and a high threshold (scores of 5 for all three responses). A chi-squared test was used to compare the validity of the responses between the chatbots. Cronbach's alpha was calculated to assess the reliability by evaluating the consistency of repeated responses from each chatbot. CONCLUSION: The results indicate that the Claude AI chatbot demonstrated superior validity and reliability compared to ChatGPT and Google Gemini, whereas Bing was found to be less reliable. These findings underscore the need for authorities to establish strict guidelines to ensure the accuracy of medical information provided by AI chatbots.
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During root development, the teeth are subjected to a variety of assaults. Due to this, the root stops forming and the closure of the apex does not take place. Root canal treatment becomes a major challenge in these cases because of the width of the canal and wide-open apices. Management of open apices includes apexogenesis in vital young permanent teeth and apexification, which is a method to induce a calcified barrier in the root. Newer concepts include regeneration and revascularization procedures, which still need to be experimented with further.
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Introduction: Tooth cervical abrasion (CA) is a prevalent non-carious cervical lesion that poses challenges for accurate diagnosis from periapical radiographs due to difficulties in assessing the lesion's extent, associated bone loss, and pulpal involvement. The presence of overlying bone structures on the palatal side when lesions are located on the buccal side, or vice versa, further complicates radiographic interpretation. So it is important to define the lesions in all three dimensions. Objective: To provide a three-dimensional descriptive classification for cervical abrasion lesions using Cone Beam Computed Tomography (CBCT). Method: A total of 50 patients with cervical abrasion were selected for the study. From these patients, teeth (n = 10) from each of the four different quadrants were chosen. A CBCT scan with a 6 × 6 cm field of view (FOV) was performed, and the DICOM files of the cervical lesions were transferred to 3-D imaging software. The CBCT images of the cervical abrasion lesions were assessed at the level of the deepest point of the lesion along the long axis of the tooth in both axial and sagittal planes. The height (A), buccolingual dimension (B), circumferential spread (C), and remaining dentine thickness (D) were evaluated and classified using new scoring criteria for each dimension. The reliability and reproducibility of the classification were assessed to ensure its clinical applicability. Conclusion: CBCT can be utilized to classify tooth cervical abrasion in endodontics, enhancing diagnosis, analysis, and treatment outcomes. This three-dimensional view facilitates easier communication among clinicians, allows for tailored treatment approaches, and opens new avenues for research.