Abstract

Over recent years, the amount and scope of scientific and clinical data in oncology has increased significantly, including but not limited to the field of electronic health data, radiographic and histological data and genomics. This growth promises a deeper understanding of malignancy and therefore personalized and more reliable oncological treatment. However, such objectives entail the creation of new methods to allow full use of the wealth of available data. Improvements in computer processing power and the advancement of algorithms have placed master learning, an artificial intelligence branch, in the field of oncology research and practice. This analysis offers a summary of the fundamentals of computer education and addresses recent advances and difficulties in the application of this technology to cancer diagnostics, prognosis, and treatment recommendations.