Objective: This article provides an overview of the implications of ChatGPT and other large language models (LLMs) for dental medicine.Overview: ChatGPT, a LLM trained on massive amounts of textual data, is adept at fulfilling various language‐related tasks. Despite its impressive capabilities, ChatGPT has serious limitations, such as occasionally giving incorrect answers, producing nonsensical content, and presenting misinformation as fact. Dental practitioners, assistants, and hygienists are not likely to be significantly impacted by LLMs. However, LLMs could affect the work of administrative personnel and the provision of dental telemedicine. LLMs offer potential for clinical decision support, text summarization, efficient writing, and multilingual communication. As more people seek health information from LLMs, it is crucial to safeguard against inaccurate, outdated, and biased responses to health‐related queries. LLMs pose challenges for patient data confidentiality and cybersecurity that must be tackled. In dental education, LLMs present fewer challenges than in other academic fields. LLMs can enhance academic writing fluency, but acceptable usage boundaries in science need to be established.Conclusions: While LLMs such as ChatGPT may have various useful applications in dental medicine, they come with risks of malicious use and serious limitations, including the potential for misinformation.Clinical Significance: Along with the potential benefits of using LLMs as an additional tool in dental medicine, it is crucial to carefully consider the limitations and potential risks inherent in such artificial intelligence technologies.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network–based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

Objectives: This study aimed to assess whether the emergency service of a major Swiss dental institution faced different demands (patient volume, treatment needs, dental care characteristics) during a lockdown, issued to mitigate the COVID-19 pandemic, compared with the weeks before and after. Materials and methods: Data of patients receiving urgent care at a university center for dental medicine (Basel, Switzerland) during the 6-week lockdown, pre-lockdown, and post-lockdown periods were retrospectively evaluated. Statistical analysis involved tests for equal proportions and logistic regression models. The level of significance was set at α=0.05. Results: The study comprised 3109 dental emergency visits in the period from February 2 to June 5, 2020. Daily caseloads increased during lockdown. Abscesses, orthodontic emergencies, and surgical follow-ups were more common during lockdown, whereas the number of dento-alveolar injuries declined (≤0.048). Urgent dental care provision involved intraoral radiographs more frequently in the pre-lockdown period compared with the following weeks (p<0.001). Among all treatments, aerosol-generating procedures dropped from 56.1% (pre-lockdown) to 21.3% during lockdown (p<0.001), while teledentistry follow-ups became more frequent (p<0.001). Patients with comorbidities sought urgent dental care less frequently during the post-lockdown period (p=0.004). Conclusions: The lockdown significantly impacted the dental emergency service in terms of patients’ diagnoses, treatment needs, and the characteristics of the urgent care that was delivered. Clinical relevance: Access to essential dental care must be monitored and safeguarded throughout the COVID-19 pandemic and beyond as deferred care entails risks for serious sequelae and persons with comorbidities may change their dental care-seeking behavior.

Large language models (LLMs) such as ChatGPT have potential applications in healthcare, including dentistry. Priming, the practice of providing LLMs with initial, relevant information, is an approach to improve their output quality. This study aimed to evaluate the performance of ChatGPT 3 and ChatGPT 4 on self-assessment questions for dentistry, through the Swiss Federal Licensing Examination in Dental Medicine (SFLEDM), and allergy and clinical immunology, through the European Examination in Allergy and Clinical Immunology (EEAACI). The second objective was to assess the impact of priming on ChatGPT’s performance. The SFLEDM and EEAACI multiple-choice questions from the University of Bern’s Institute for Medical Education platform were administered to both ChatGPT versions, with and without priming. Performance was analyzed based on correct responses. The statistical analysis included Wilcoxon rank sum tests (α=0.05). The average accuracy rates in the SFLEDM and EEAACI assessments were 63.3% and 79.3%, respectively. Both ChatGPT versions performed better on EEAACI than SFLEDM, with ChatGPT 4 outperforming ChatGPT 3 across all tests. ChatGPT 3's performance exhibited a significant improvement with priming for both EEAACI (p=0.017) and SFLEDM (p=0.024) assessments. For ChatGPT 4, the priming effect was significant only in the SFLEDM assessment (p=0.038). The performance disparity between SFLEDM and EEAACI assessments underscores ChatGPT’s varying proficiency across different medical domains, likely tied to the nature and amount of training data available in each field. Priming can be a tool for enhancing output, especially in earlier LLMs. Advancements from ChatGPT 3 to 4 highlight the rapid developments in LLM technology. Yet, their use in critical fields such as healthcare must remain cautious owing to LLMs’ inherent limitations and risks.