A digital twin (DT) is a three-part idea, which includes a virtual counterpart, a physical model, and the interaction between the two. This intersection of medicine and computer science represents a new area with numerous possible applications. DT technology can evaluate the correlations between a physical cancer patient and a comparable digital counterpart to isolate predictors of disease. DT can be created in healthcare for both patients and the disease risk assessment and therapy process, and they can be used to inform quantitatively adaptive risk assessment, diagnosis, and therapy decision-making, as well as personalization and optimization of health outcomes, prediction and prevention of adverse events, and intervention planning. In an ideal world, the DT concept may be used to patients to enhance diagnoses and therapy. The goal is to (1) create an unlimited number of replicas of network models of all phenotypic, molecular, and environmental factors related to disease mechanisms in individual patients; (2) computationally treat those DTs with thousands of drugs to find the best-performing drug; and (3) treat the patient with this drug and observe the side effects. To address multistage risk assessment and therapy selection models, which include both related disease and side-effect considerations in which a digital replica or DT of a physical process or entity is virtually recreated, with similar elements and dynamics, to achieve real-time optimization and testing, is used. This chapter presents the notion that data science may supplement clinical expertise to scientifically guide disease diagnosis, treatment planning, and prognosis. In particular, digital twins could forecast disease obstacles by using them in precision medicine, disease care and treatment modeling, machine learning, and predictive analytics and combining distinct scales of clinician viewpoints.