  A key challenge for the development and deployment of artificial intelligence
(AI) solutions in radiology is solving the associated data limitations.
Obtaining sufficient and representative patient datasets with appropriate
annotations may be burdensome due to high acquisition cost, safety limitations,
patient privacy restrictions or low disease prevalence rates. In silico data
offers a number of potential advantages to patient data, such as diminished
patient harm, reduced cost, simplified data acquisition, scalability, improved
quality assurance testing, and a mitigation approach to data imbalances. We
summarize key research trends and practical uses for synthetically generated
data for radiological applications of AI. Specifically, we discuss different
types of techniques for generating synthetic examples, their main application
areas, and related quality control assessment issues. We also discuss current
approaches for evaluating synthetic imaging data. Overall, synthetic data holds
great promise in addressing current data availability gaps, but additional work
is needed before its full potential is realized.
