The SEARCH project is an ongoing effort to develop secure ways of using healthcare data for research and AI development without exposing sensitive patient information. It explores the use of synthetic data (artificially generated datasets that mimic real medical data) to allow hospitals, researchers, and AI developers to work with valuable insights while maintaining privacy.
By combining Federated Learning and Synthetic Data Generation (SDG), SEARCH aims to enable data-driven advancements in diagnosis, treatment, and personalized medicine without requiring direct access to original patient records.
The project focuses on generating synthetic versions of electronic health records, signal data, biomarkers, medical images, and videos, addressing key challenges in healthcare data sharing, including legal, ethical, and security concerns. The goal is to make medical AI development more accessible while ensuring compliance with privacy regulations.
