Augmenting digital twins with federated learning in medicine

Clinical digital twins are virtual representations of patients that throughout patient's treatment course, making them valuable for various applications for predicting patient's treatment outcomes.1, 2 Hailed as a fundamental shift in medical treatment, digital twins face major challenges, particularly regarding privacy concerns before adoption of digital twins. We identify federated learning as a unique solution to this challenge that also enables proliferation and active sharing of digital twins technology without the necessity to reveal patient information.

Augmenting digital twins with federated learning in medicine - The Lancet Digital Health