Aim: Chatbot Generative Pre‐trained Transformer (ChatGPT) is a generative artificial intelligence (AI) software based on large language models (LLMs), designed to simulate human conversations and generate novel content based on the training data it has been exposed to. The aim of this study was to evaluate the consistency and accuracy of ChatGPT‐generated answers to clinical questions in endodontics, compared to answers provided by human experts.Methodology: Ninety‐one dichotomous (yes/no) questions were designed and categorized into three levels of difficulty. Twenty questions were randomly selected from each difficulty level. Sixty answers were generated by ChatGPT for each question. Two endodontic experts independently answered the 60 questions. Statistical analysis was performed using the SPSS program to calculate the consistency and accuracy of the answers generated by ChatGPT compared to the experts. Confidence intervals (95%) and standard deviations were used to estimate variability.Results: The answers generated by ChatGPT showed high consistency (85.44%). No significant differences in consistency were found based on question difficulty. In terms of answer accuracy, ChatGPT achieved an average accuracy of 57.33%. However, significant differences in accuracy were observed based on question difficulty, with lower accuracy for easier questions.Conclusions: Currently, ChatGPT is not capable of replacing dentists in clinical decision‐making. As ChatGPT's performance improves through deep learning, it is expected to become more useful and effective in the field of endodontics. However, careful attention and ongoing evaluation are needed to ensure its accuracy, reliability and safety in endodontics.

The artificial intelligence (AI) software program ChatGPT is based on large language models (LLMs) and is widely accessible. However, in prosthodontics, little is known about its performance in generating answers. The purpose of this study was to determine the performance of ChatGPT in generating answers about removable dental prostheses (RDPs) and tooth-supported fixed dental prostheses (FDPs). Thirty short questions were designed about RDPs and tooth-supported FDP, and 30 answers were generated for each of the questions using ChatGPT-4 in October 2023. The 900 generated answers were independently graded by experts using a 3-point Likert scale. The relative frequency and absolute percentage of answers were described. Accuracy was assessed using the Wald binomial method, while repeatability was evaluated using percentage agreement, Brennan and Prediger coefficient, Conger generalized Cohen kappa, Fleiss kappa, Gwet AC, and Krippendorff alpha methods. Confidence intervals were set at 95%. Statistical analysis was performed using the STATA software program. The performance of ChatGPT in generating answers related to RDP and tooth-supported FDP was limited. The answers showed a reliability of 25.6%, with a confidence range between 22.9% and 28.6%. The repeatability ranged from substantial to moderate. The results show that currently ChatGPT has limited ability to generate answers related to RDPs and tooth-supported FDPs. Therefore, ChatGPT cannot replace a dentist, and, if professionals were to use it, they should be aware of its limitations.

Knowing how to diagnose effectively and efficiently is a fundamental skill that a good dental professional should acquire. If students perform a greater number of clinical cases, they will improve their performance with patients. In this sense, virtual patients with artificial intelligence offer a controlled, stimulating, and safe environment for students. To assess student satisfaction after interaction with an artificially intelligent chatbot that recreates a virtual patient, a descriptive cross-sectional study was carried out in which a virtual patient was created with artificial intelligence in the form of a chatbot and presented to fourth and fifth year dental students. After several weeks interacting with the AI, they were given a survey to find out their assessment. A total of 193 students participated. A large majority of the students were satisfied with the interaction (mean 4.36), the fifth year students rated the interaction better and showed higher satisfaction values. The students who reached a correct diagnosis rated this technology more positively. Our research suggests that the incorporation of this technology in dental curricula would be positively valued by students and would also ensure their training and adaptation to new technological developments.

In recent years, there has been an increase in the incidence of dental wear; thus, an early diagnosis is important. Conventional methods of diagnosis are based primarily on the visual abilities of the dentist, and therefore the use of new technologies for the detection of dental wear may be very useful. The aim of the study was to analyze the sensitivity and specificity of the intraoral scanner for measuring dental wear, as well as to evaluate patients’ satisfaction with the use of the scanner. The study was conducted with 46 volunteers who underwent three intraoral analyses: a first baseline scanning, a second scanning after 6 months and a final scanning after one year performed by four operators divided into two groups. One of the operators performed the visual analysis of dental wear, and the other performed the analysis using the intraoral scanner 3M™ True Definition intraoral scanner (ESPE, Seefeld, Germany). The data obtained from the intraoral scanner showed levels of specificity and sensitivity that enable the intraoral scanner to be used as a diagnostic tool in the assessment of tooth wear. The participants also showed a high degree of satisfaction with the scanner as a communication tool.

(1) Background: Large language models (LLMs) are revolutionising various scientific fields by providing advanced support tools. However, the effectiveness of these applications depends on extensive, up-to-date databases to ensure certainty and predictive power. Transparency about information sources in Medicine remains a significant issue. (2) Methods: To evaluate Google Gemini’s accuracy and reproducibility in endodontic diagnosis and treatment, 60 questions were designed based on the European Society of Endodontology Position Statements. Thirty questions were randomly selected and answered using Gemini during April 2023. Two endodontic experts independently scored the answers using a 3-point Likert scale. Discrepancies were resolved by a third expert. The relative frequency and absolute percentage of responses were detailed. Accuracy was assessed using the Wald binomial method, and repeatability was assessed using percentage agreement, Brennan and Prediger’s coefficient, Conger’s generalised kappa, Fleiss’ kappa, Gwet’s AC, and Krippendorff’s alpha, all with 95% confidence intervals. Statistical analysis was performed using STATA software. (3) Results: A total of 900 answers were generated. The percentage of correct answers varied from 0% to 100% per question. Overall accuracy was 37.11% with a 95% confidence interval of 34.02–40.32%; (4) Conclusions: Gemini is not currently designed for medical use and therefore needs to be used with caution when considered for this purpose.

Dental wear arises from mechanical (attrition or abrasion) and chemical (erosion) factors. Despite its prevalence and clinical significance, accurately measuring and understanding its causes remain challenging in everyday practice. This one-year study with 39 participants involved comprehensive examinations and full-arch intraoral scans at the start and after 12 months. Volume loss exceeding 100 µ on each tooth’s surfaces (buccal, lingual/palatine and incisal/occlusal) was measured by comparing three-dimensional scans from both time points. This study also assessed factors such as abrasion and erosion through clinical exams and questionnaires. There were no significant differences in dental wear in participants with sleep bruxism. However, noticeable wear occurred in the front teeth of those with waking bruxism and joint-related symptoms. Increased wear was associated with frequent consumption of acidic drinks, regular swimming, dry mouth, nocturnal drooling and heartburn, while no significant wear was found in patients with reflux. The used methodology proved effective in accurately assessing the progression of dental wear, which is important as many patients may initially be asymptomatic. The variability observed in dental wear patterns underscores the need to develop specific software applications that allow immediate and efficient comparison of wear areas based on extensive analysis of patient databases.