Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient’s survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. For example, machine learning has the potential to materially reduce cancer mortality. It will do so primarily through more accurate diagnostics and earlier detection. In recent years, multiple researchers have applied machine learning methods successfully to a range of diagnostic tasks in areas as diverse as radiology, pathology, dermatology, urology or gynecology and deep learning technology in different types of cancer such as lung cancer, breast cancer, thyroid cancer, and etc. is done.

The ability of cancer-oriented cognitive computers to provide clinicians with access to accurate and timely information and treatment guidelines will benefit patients and accelerate the dissemination of knowledge into clinical practice worldwide. Medical or support staff in remote areas of the world with minimal cancer knowledge may rely on cancer cognitive computers to enhance and optimize cancer care delivery. This “democratization” of cancer knowledge and information will offer access to equitable care, no matter who and where patients are. Cognitive computers can optimize clinical research and trials, and reduce bureaucracy and cost, by abstracting and detailing research data (patient information, medications, therapy, dose schedules, response, adverse effects) in objective and precise forms. In years to come, large databases using artificial intelligence will complement each other and may be incorporated into large cancer open networks that inform, educate, and help cancer treatment and research.