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Oral cancer constitutes around 2.1% and it is the sixth-most common malignancy worldwide and the third-most common type of malignancy in India. The purpose of this systematic review is to find the prediction of survival rate among oral cancer patients using artificial intelligence (AI) and its forms like machine learning. Suitable articles were identified by searching PubMed, Trip database, Cochrane, and Google Scholar host databases. The search was done with the help of PIO analysis where the population stands for oral cancer patients, the intervention given here were AI and its subsets and the outcome were diagnosis and survival prediction of oral cancer. The screening of the titles and abstracts was done, and only those articles that fulfilled the eligibility criteria were selected. The search resulted in 451 articles, of which only six articles that fulfilled the criteria were included. The studies showed that AI models were able to predict the 5-year survival rate among oral cancer patients. The accuracy of the decision tree classifier, logistic regression, and boosted decision tree models were 76%, 60%, and 88.7%, respectively. Modern age diagnosed people tend to have a longer survival rate than those diagnosed in the past. The limitation was that these studies were created using retrospective cohorts, but for validation, they must be compared with prospective cohorts. These studies are important for identification and survival prediction, which will contribute to future advancements, change in the treatment plan, and reduce health-care problems.

Statement of the Problem: It is essential to address caries risk at an early stage for the prevention of dental caries. Mobile application CaRisk is designed in a particular way to self-assess the dental caries risk by the individual’s themselves. Purpose: The current study aimed to assess the dental caries risk among age groups 5-6 and 35-44 using self-assessment caries risk mobile application CaRisk and compare it with the deft and DMFT values. Materials and Method: This cross-sectional study was conducted in Chennai, India; to evaluate the risk of dental caries in children aged 5 to 6 and adults aged 35 to 44. The scores of the mobile application CaRisk and the decayed- extracted-filled teeth (deft)/ decayed-missing-filled-teeth (DMFT) caries risk assessment were evaluated. Descriptive statistics were performed. The risk category was determined by frequency. Chi-square analysis was done to determine whether the DMFT scores and the CaRisk mobile app were associated. The correlation was performed between the CaRisk mobile application and DMFT scores. Results: Association was found between the caries risk assessment score of the mobile application CaRisk and the DMFT and deft scores of the adults and children for both the age groups 5-6 and 35-44 years respectively and it indicates that it was found to be statistically significant. Pearson’s correlation was performed to assess the strength of association and R-values obtained for the age group 5-6 and 35-44 years respectively, which was statistically significant (0.892 and 0.840). Conclusion: This CaRisk mobile application scores correlate with the deft and DMFT scores and it is an effective self-diagnosis tool for assessing dental caries risk assessment. Further, it is suggested that the mobile application CaRisk should be tested among a huge population. [ABSTRACT FROM AUTHOR]