Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support

INSAFEDARE aims to provide advanced technologies that address the challenges and exploit the opportunities surrounding real-world and synthetic data-driven validation of medical devices in support of cost-effective and high-assurance regulatory decision-making. INSAFEDARE is investigating how synthetic datasets can be used to establish assurance in advance of formal certification processes, thereby reducing risks for device developers and enabling more efficient resource usage by medical regulatory bodies. The project will develop technologies for the discovery, integration, and query of multiple datasets, as well as support for the sustainable, dynamic, and through-life surveillance of devices, capturing the impact of new evidence offered by newly published datasets.

Learn more: https://www.insafedare.org/