Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy 7. Data like this is hard to obtain, because health data is highly sensitive and its usage is tightly regulated 6. Modern DL models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data 2, 3, 4, 5.įor example, training an AI-based tumour detector requires a large database encompassing the full spectrum of possible anatomies, pathologies, and input data types. Research on artificial intelligence (AI), and particularly the advances in machine learning (ML) and deep learning (DL) 1 have led to disruptive innovations in radiology, pathology, genomics and other fields.
