Introduction: Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance in the progress of AI is access to diverse datasets which train DL models ensuring optimal performance, comparable to subject experts. However, administration of these traditionally acquired datasets is challenging due to privacy regulations and the extensive manual annotation required by subject experts. Biases such as ethical, socioeconomic and class imbalances are also incorporated during the curation of these datasets, limiting their overall generalizability. These challenges prevent their accrual at a larger scale for training DL models. Methods: Generative AI techniques can be useful in the production of Synthetic Datasets (SDs) that can overcome issues affecting traditionally acquired datasets. Variational autoencoders, generative adversarial networks and diffusion models have been used to generate SDs. The following text is a review of these generative AI techniques and their operations. It discusses the chances of SDs and challenges with potential solutions which will improve the understanding of healthcare professionals working in AI research. Conclusion: Synthetic data customized to the need of researchers can be produced to train robust AI models. These models, having been trained on such a diverse dataset will be applicable for dissemination across countries. However, there is a need for the limitations associated with SDs to be better understood, and attempts made to overcome those concerns prior to their widespread use.

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI’s decision-making processes. A sample of 70 photographs was used to directly compare the application’s performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level.

Objective: This study underscores the transformative role of Artificial Intelligence (AI) in healthcare, particularly the promising applications of Large Language Models (LLMs) in the delivery of post-operative dental care. The aim is to evaluate the performance of an embedded GPT model and its comparison with ChatGPT-3.5 turbo. The assessment focuses on aspects like response accuracy, clarity, relevance, and up-to-date knowledge in addressing patient concerns and facilitating informed decision-making. Material and methods: An embedded GPT model, employing GPT-3.5-16k, was crafted via GPT-trainer to answer postoperative questions in four dental specialties including Operative Dentistry & Endodontics, Periodontics, Oral & Maxillofacial Surgery, and Prosthodontics. The generated responses were validated by thirty-six dental experts, nine from each specialty, employing a Likert scale, providing comprehensive insights into the embedded GPT model’s performance and its comparison with GPT3.5 turbo. For content validation, a quantitative Content Validity Index (CVI) was used. The CVI was calculated both at the item level (I-CVI) and scale level (S-CVI/Ave). To adjust I-CVI for chance agreement, a modified kappa statistic (K*) was computed. Results: The overall content validity of responses generated via embedded GPT model and ChatGPT was 65.62% and 61.87% respectively. Moreover, the embedded GPT model revealed a superior performance surpassing ChatGPT with an accuracy of 62.5% and clarity of 72.5%. In contrast, the responses generated via ChatGPT achieved slightly lower scores, with an accuracy of 52.5% and clarity of 67.5%. However, both models performed equally well in terms of relevance and up-to-date knowledge. Conclusion: In conclusion, embedded GPT model showed better results as compared to ChatGPT in providing post-operative dental care emphasizing the benefits of embedding and prompt engineering, paving the way for future advancements in healthcare applications.

Introduction: Artificial Intelligence (AI) is a rapidly evolving technology, with various applications in dentistry including diagnosis, treatment planning, and prognosis. There are various AI-based applications for dental practitioners, however, their real-world evaluation through deployement studies is scarce, as most of the studies are validation studies. This review explores the potential pitfalls of focusing solely on technical performance metrics when evaluating AI-based applications in dentistry while overlooking the importance of clinical applicability. Methods: An electronic search was performed on PubMed and Scopus while a manual search was conducted on Google Scholar “Dentistry”, “Dental”, “Artificial Intelligence”, “Deep Learning, “Machine Learning”, “Applications”, “Diagnocat”, “CephX”, “Denti.AI”, “VideaAI”, “Smile Designer”, “Overjet”, “DentalXR.AI”, “Smilo.AI”, “Smile.AI”, “Pearl”, “AI deployment challenges in dental practice”, “AI for treatment planning in dentistry”, “AI in dental imaging”, and “AI-based diagnosis in dentistry”. Results: The electronic search yielded a total of 34 studies, while 10 additional studies were obtained through a manual search, resulting in a total of 44 studies included in this review. Among the 44 studies analyzed, 26 studies were retrospective, while 7 studies utilized a comparative design. The remaining studies comprised of 3 observational, 5 validation, 2 cross-sectional, and 1 prospective study. Further to evaluate the identified applications, relevant companies were contacted via email. Only one company’s representative responded, offering a limited trial version which was insufficient for evaluating the application’s effectiveness. AI technologies may offer lots of benefits for dental practice by enhancing patient-health-based outcomes, however, real-world applications are necessary to ensure its safety. Conclusion: This work highlights the need for conducting deployment studies for such AI-based dental applications to translate and implement them into dental practice. Collaboration with stakeholders and dental practitioners to assess the use of such applications is of paramount importance.