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With the arrival of the era of big data, increasing attention has been drawn to the application of artificial intelligence (AI) in the medical field. AI has many advantages, such as objectivity, accuracy, minimal invasiveness, time savings and high efficiency. Therefore, the combination of AI with dental diagnosis and treatment can help dentists improve work efficiency and save medical resources, offering potential significant benefits for dental application. At present, AI has been gradually integrated into prosthodontics, oral and maxillofacial surgery, orthodontics, endodontics and periodontics. The AI system can realize automatic tooth preparation, automatic tooth arrangement and implantology. Deep learning can be used to assist in diagnosing maxillary sinus inflammation, predicting the complications of tooth extraction and improving the accuracy of osteotomy. The AI system can also provide significant clues for the diagnosis, treatment and prognosis of oral and maxillofacial tumors. The breakthrough brought by AI in cephalometric and the assessment of facial attractiveness of patients has promoted the development of intelligent and personalized orthodontic treatment. Deep learning and analysis of medical images also promote the accuracy of root canal therapy as well as the diagnosis and treatment of periodontal diseases. AI technology has realized the leap from digitalization to automation and intelligence in oral diagnosis and treatment, and its application potential in the oral field should not be underestimated. Based on the concepts of AI, this paper will focus on the application of artificial intelligence in various oral clinical fields and briefly introduce its advantages, problems and future.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases. [ABSTRACT FROM AUTHOR]