  Randomized clinical trials, while often viewed as the highest evidentiary bar
by which to judge the quality of a medical intervention, are far from perfect.
In silico imaging trials are computational studies that seek to ascertain the
performance of a medical device by collecting this information entirely via
computer simulations. The benefits of in silico trials for evaluating new
technology include significant resource and time savings, minimization of
subject risk, the ability to study devices that are not achievable in the
physical world, allow for the rapid and effective investigation of new
technologies and ensure representation from all relevant subgroups. To conduct
in silico trials, digital representations of humans are needed. We review the
latest developments in methods and tools for obtaining digital humans for in
silico imaging studies. First, we introduce terminology and a classification of
digital human models. Second, we survey available methodologies for generating
digital humans with healthy and diseased status and examine briefly the role of
augmentation methods. Finally, we discuss the trade-offs of four approaches for
sampling digital cohorts and the associated potential for study bias with
selecting specific patient distributions.
