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In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]