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Augmenting digital twins with federated learning in medicine

Updated: Jun 9, 2023

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.


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