Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations. A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared. ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions. The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Gingivitis is one of the most prevalent plaque-initiated dental diseases globally. It is challenging to maintain satisfactory plaque control without continuous professional advice. Artificial intelligence may be used to provide automated visual plaque control advice based on intraoral photographs. Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement. A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50. Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.

Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers. Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions. 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel's assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process. GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures. This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities.

Oral diseases are largely preventable. However, as the number of older adults is expected to increase, along with the high cost and various barriers to seeking continuous professional care, a sustainable approach is needed to assist older adults in maintaining their oral health. Mobile health (mHealth) technologies may facilitate oral disease prevention and management through oral health education. This review aims to provide an overview of existing evidence on using mHealth to promote oral health through education among older adults. A literature search was performed across five electronic databases. A total of five studies were identified, which provided low to moderate evidence to support using mHealth among older adults. The selected studies showed that mHealth could improve oral health management, oral health behavior, and oral health knowledge among older adults. However, more quality studies regarding using mHealth technologies in oral health management, oral health behavior, and oral health knowledge among older adults are needed.

Background: Dental prostheses, which aim to replace missing teeth and to restore patients’ appearance and oral functions, should be biomimetic and thus adopt the occlusal morphology and three-dimensional (3D) position of healthy natural teeth. Since the teeth of an individual subject are controlled by the same set of genes (genotype) and are exposed to mostly identical oral environment (phenotype), the occlusal morphology and 3D position of teeth of an individual patient are inter-related. It is hypothesized that artificial intelligence (AI) can automate the design of single-tooth dental prostheses after learning the features of the remaining dentition. Materials and methods: This article describes the protocol of a prospective experimental study, which aims to train and to validate the AI system for design of single molar dental prostheses. Maxillary and mandibular dentate teeth models will be collected and digitized from at least 250 volunteers. The (original) digitized maxillary teeth models will be duplicated and processed by removal of right maxillary first molars (FDI tooth 16). Teeth models will be randomly divided into training and validation sets. At least 200 training sets of the original and the processed digitalized teeth models will be input into 3D Generative Adversarial Network (GAN) for training. Among the validation sets, tooth 16 will be generated by AI on 50 processed models and the morphology and 3D position of AI-generated tooth will be compared to that of the natural tooth in the original maxillary teeth model. The use of different GAN algorithms and the need of antagonist mandibular teeth model will be investigated. Results will be reported following the CONSORT-AI.

Background/Objectives: This study aims to evaluate the response accuracy and quality of three AI chatbots—GPT-4.0, Claude-2, and Llama-2—in answering multiple-choice questions in prosthodontic and restorative dentistry. Methods: A total of 191 text-based multiple-choice questions were selected from the prosthodontic and restorative dentistry sections of the United States Integrated National Board Dental Examination (INBDE) (n = 80) and the United Kingdom Overseas Registration Examination (ORE) (n = 111). These questions were inputted into the chatbots, and the AI-generated answers were compared with the official answer keys to determine their accuracy. Additionally, two dental specialists independently evaluated the rationales accompanying each chatbot response for accuracy, relevance, and comprehensiveness, categorizing them into four distinct ratings. Chi-square and post hoc Z-tests with Bonferroni adjustment were used to analyze the responses. The inter-rater reliability for evaluating the quality of the rationale ratings among specialists was assessed using Cohen’s Kappa (κ). Results: GPT-4.0 (65.4%; n = 125/191) demonstrated a significantly higher proportion of correctly answered multiple-choice questions when compared to Claude-2 (41.9%; n = 80/191) (p < 0.017) and Llama-2 (26.2%; n = 50/191) (p < 0.017). Significant differences were observed in the answer accuracy among all of the chatbots (p < 0.001). In terms of the rationale quality, GPT-4.0 (58.1%; n = 111/191) had a significantly higher proportion of “Correct Answer, Correct Rationale” than Claude-2 (37.2%; n = 71/191) (p < 0.017) and Llama-2 (24.1%; n = 46/191) (p < 0.017). Significant differences were observed in the rationale quality among all of the chatbots (p < 0.001). The inter-rater reliability was very high (κ = 0.83). Conclusions: GPT-4.0 demonstrated the highest accuracy and quality of reasoning in responding to prosthodontic and restorative dentistry questions. This underscores the varying efficacy of AI chatbots within specialized dental contexts.