This reprint covers some significant impacts in the recent research in both the private and public sectors of cancer diagnosis and therapy, in which Artificial Intelligence (AI) and Machine Learning are significant. This reprint is also a collection of forty different complex and challenging problems arranged in five groups: AI in prognosis, grading, and prediction, AI in clinical image analysis, AI models for pathological diagnosis, ML and statistical models for molecular cancer diagnostics and genetics, and AI in triage, risk stratification, and screening cancer, which are all focused on using AI in cancer diagnosis and therapy.

All the necessary concepts, solutions, methodologies, and references are supplied except for some fundamental knowledge that is well-known in the general ?elds of AI and cancer diagnosis and therapy. The readers may, therefore, gain the main concepts of each chapter, with as little of a need as possible to refer to the concepts of the other chapters and references. The readers may hence start to read one or more chapters of the book for their own interests.