Synthetic Data and Health Privacy - Abgrall, Gwénolé; Monnet, Xavier; Arora, Anmol (JAMA : the journal of the American Medical Association, 2025)
Abgrall et al discuss generative artificial intelligence and safeguarding privacy by using synthetic data as a substitute for private health data. Synthetic data, designed to emulate real patient characteristics without revealing identifiable information, offer a potential solution to this conundrum. They can be generated by using rule-based techniques, using statistical modeling to approximate real data distributions, or training generative artificial intelligence (GAI) models such as generative adversarial networks on real data to capture underlying structures and create more complex synthetic datasets. Creating synthetic data from real personal data is considered processing under the European Union's General Data Protection Regulation. However, whether and when synthetic data remain personal data--and thus subject to the regulation--remains a complex issue. Recent legislation points toward synthetic data as not being considered personal data. Clinicians and developers should avoid using sensitive data to train, fine-tune, or use GAI models, reducing the risk of privacy breaches.