  Synthetic data are becoming a critical tool for building artificially
intelligent systems. Simulators provide a way of generating data systematically
and at scale. These data can then be used either exclusively, or in conjunction
with real data, for training and testing systems. Synthetic data are
particularly attractive in cases where the availability of ``real'' training
examples might be a bottleneck. While the volume of data in healthcare is
growing exponentially, creating datasets for novel tasks and/or that reflect a
diverse set of conditions and causal relationships is not trivial. Furthermore,
these data are highly sensitive and often patient specific. Recent research has
begun to illustrate the potential for synthetic data in many areas of medicine,
but no systematic review of the literature exists. In this paper, we present
the cases for physical and statistical simulations for creating data and the
proposed applications in healthcare and medicine. We discuss that while
synthetics can promote privacy, equity, safety and continual and causal
learning, they also run the risk of introducing flaws, blind spots and
propagating or exaggerating biases.
