The present scenario in healthcare field that is evolving fast decision making to handle expanding request on clinical and regulatory data to understand patients legal and clinical necessities. Concerning this, making decisions on medicinal services has changed into an vital, intricate and unstructured issues. The domain of healthcare intelligence constitutes knowledge discovery database, the clinical support systems, and intelligence risk detection model. Overtreatment, poor execution of inpatient care, and inability to receive best practices for preventive care and patient wellbeing has direct effects on both human services expenses and patient results. The accessibility of electronic inpatient information and the treatment methods recommend the potential for the utilization of computerized reasoning and machine learning strategies to enhance the quality and lower the cost of inpatient care. The challenge for artificial intelligence (AI) in medicinal services is to create approaches that can be easily connected to most of the patients, checking huge amounts of information to naturally distinguish issues and menaces to patient security and to find new accepted procedures of patient care. A majority of high-hazard patients can be at the same time observed without patient mediation. Both question-answer and odd example identification included in the AI approaches.