Patient understanding of their treatment need or aesthetic classification may not always be as accurate as that of orthodontists. This may be a cause for concern when an orthodontist finds a certain condition to be severe, and a patient who does not agree may limit their treatment needs.

Artificial intelligence is the ability of machines to work like humans. The concept initially began with the advent of mathematical models which gave calculated outputs based on inputs fed into the system. This was later modified with the introduction of various algorithms which can either give output based on overall data analysis or by selection of information within previous data. It is steadily becoming a favoured mode of treatment due to its efficiency and ability to manage complex conditions in all  specialities. In dentistry, artificial intelligence has also popularised over the past few decades. They have been found useful fordiagnosis in restorative dentistry, oral pathology and oral surgery. In orthodontics, they have been utilised for diagnosis, assessment of treatment needs, cephalometrics, treatment planning and orthognathic surgeries etc. The current literature review was planned to highlight the uses of artificial intelligence in dentistry, specifically in orthodontics and orthognathic surgery. Continuous...

Objective: To evaluate the role of cancer stem cell biomarkers in diagnosis and prognosis of OSCC patients. Methods: The search strategy was entered into PubMed NLM, EBSCO CINAHL, EBSCO Dentistry & Oral Sciences Source, Wiley Cochrane Library, and Scopus. The full text eligible studies (n=7) were assessed for their quality using the JBI Critical Appraisal Checklist to evaluates the methodological quality of the studies based on possibility of bias in its design, conduct, and analysis. Selected studies were further analysed based on different parameters such as publication year, sample size, and outcomes. Results: A total of 432 studies were identified through the search strategy. A total of 306 records were removed before screening either because of duplication or marked ineligible by the automation tools. The screened records were 126 out of which 104 were removed as they were not conducted on OSCC. Twenty-two reports were sought for retrieval, however, we could not find the full text of 3 studies and12 studies were excluded because the biomarkers were not associated with cancer stem cells. The most common cancer stem cell biomarkers associated with OSCC were MCT1,VEGF-A, GD15, HIF1 alpha, Ki67, Hsp 70, Cyclin D1, and CD44. Conclusions: Various stem cell biomarkers have been found to have diagnostic and prognostic role in oral squamous cell carcinoma such as Cyclin D1, VEGF-A, GD15, and CD44. They can be used to predict the overall survival rate, local progression-free survival rate, and distant metastasis-free survival rate in Head and Neck cancer patients Continued...