In silicotrial methods promise to improve the path to market for both medicines and medical devices, targeting the development of products, reducing reliance on animal trials, and providing adjunct evidence to bolster regulatory submissions. In silicotrials are only as good as the simulated data which underpins them, consequently, often the most difficult challenge when creating robust in silicomodels is the generation of simulated measurements or even virtual patients that are representative of real measurements and patients. This article digests the current state of the art for generating synthetic patient data outside the context of in silicotrials and outlines potential synergies to unlock the potential of in silicotrials using virtual populations, by exploiting synthetic patient data to model effects on a more diverse and representative population. Synthetic data could be defined as artificial data that mimic the properties and relationships in real data. Recent advances in synthetic data generation methodologies have allowed for the generation of high-fidelity synthetic data that are both statistically and clinically, indistinguishable from real patient data. Other experimental work has demonstrated that synthetic data generation methods can be used for selective sample boosting of underrepresented groups. This article will provide a brief outline of synthetic data generation approaches and discuss how evaluation frameworks developed to assess synthetic data fidelity and utility could be adapted to evaluate the similarity of virtual patients used for in silicotrials, to real patients. The article will then discuss outstanding challenges and areas for further research that would advance both synthetic data generation methods and in silicotrial methods. Finally, the article will also provide a perspective on what evidence will be required to facilitate wider acceptance of in silicotrials for regulatory evaluation of medicines and medical devices, including implications for post marketing safety surveillance.