Recent advances in both the biological and computer sciences have spurred researchers to pay greater attention to the role of computa-tional methods in the broad sphere of cancer research. Specific focus has been given to the demonstrated benefits of artificial intelligence (AI) and machine learning approaches when compared to current methods for the diagnosis and treatment of cancer. An artificial neural network is a form of AI based on algorithms that mimic human brain function. Neural networks are especially useful in the interpretation of nonlinear data, which is commonly encountered in biological research studies. Neural networking technologies may be used to diag-nose cancer more easily and effectively than traditional methods as they decrease the need for invasive procedures and interpreting the results of imaging methods. Additionally, neural networks have been trained to analyze individual prognoses and treatment plans with an accuracy comparable to that of experienced physicians. Advances such as these aid both medical professionals and patients in making optimal health care decisions. As large-scale computing initiatives – such as the recent Microsoft project aiming to “solve” cancer with computer science – move forward, it has become increasingly apparent that the future of medical research will involve technologies such as neural networking and other forms of AI.