Augmenting digital twins with federated learning in medicine.

Abstract

Providing increasingly personalized treatments to patients is a major goal of precision medicine, and digital twins are an emerging paradigm to support this goal. A clinical digital twin is a digital representation of a patient and can be used to deliver personalized treatment recommendations. However, the centralized data collection to support and train digital twin models is already brushing up against patient privacy restrictions. We posit that the use of federated learning, an approach to decentralized machine learning model training, can support digital twins’ performance for clinical applications. We emphasize that the combination of the two could alleviate privacy concerns while bolstering machine learning model performance and resulting predictions.