Using Artificial Intelligence to Predict Clinical Requirements in Healthcare

Abstract

The world is now undergoing a revolution because of advances in artificial intelligence. In recent decades, it has been used in almost all sectors, including medical forecasting. Medical prediction attempts to forecast the risk of getting a disease, as well as the disease's survival and geographical spread. Current evidence-based therapy relies heavily on prediction, the healthcare sector is one of the most populous and rapidly expanding AI markets. The use of genetics, wearable sensors, biotechnology, and artificial intelligence increases the amount of healthcare data available and accelerates the development of analytics tools, laying the groundwork for precision medicine; advances in identifying diseases while sparing patients from invasive tests; and enables a diagnosis and treatment plan that are tailored to the individual patient's needs, environment, and lifestyle. As part of this study, we provide a survey of current practices for disease management. Artificial intelligence approaches, like machine learning (ML) and deep learning (DL) methods, have become more popular and have increased the effectiveness of both diagnosis and prognosis.