Synthetic patient data in health care: a widening legal loophole
Artificial intelligence in health care has received widespread attention, focused on deductive systems that analyse datasets to learn patterns that would be infeasible to programme. However, more recently, another form of artificial intelligence has emerged: generative adversarial networks (GANs). GANs is a form of artificial intelligence with the purpose of creating high-fidelity fake data. The artificial intelligence system is provided with a dataset of real data and learns to produce new data that retains the overall properties of the original dataset but is artificial. Synthetic data has received considerable attention as a method of protecting patient privacy and augmenting clinical research. Synthetic data carries the ability to create fake patient records and fake medical imaging that is truly non-identifiable because the data does not relate to any real individual. In a sense, the synthetic data is a derivative of the original real data but no synthetic datapoint can be attributed to a single real datapoint.1
Via The Lancet.