The field of dental research is witnessing significant advancements in automated diagnosis and imaging, driven by the development of innovative datasets and deep learning models. A key trend is the creation of large-scale, high-quality datasets for training and evaluating automated diagnostic systems, addressing the longstanding issue of data scarcity in dental research. These datasets are enabling the development of more accurate and reliable computer-aided diagnosis (CAD) applications, which have the potential to improve diagnostic accuracy and reduce the workload of dental professionals. Another area of focus is the application of generative models, such as GANs, to synthesize realistic dental radiographs, which can help alleviate data scarcity and improve the robustness of CAD systems. Furthermore, researchers are exploring the use of deep learning-based segmentation techniques to automate tasks such as margin line generation for dental crowns, which can improve the efficiency and accuracy of dental treatments. Notable papers in this area include: PerioDet, which proposes a clinical-oriented apical periodontitis detection paradigm and releases a large-scale panoramic radiograph benchmark. PanoGAN, which develops a deep generative model for synthesizing dental panoramic radiographs. AlphaDent, which presents a new dataset for automated tooth pathology detection. Mesh-based segmentation for automated margin line generation on incisors receiving crown treatment, which proposes a new framework for determining margin lines automatically and accurately using deep learning.