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. 2023 May;5(5):e251-e253.
doi: 10.1016/S2589-7500(23)00044-4.

Augmenting digital twins with federated learning in medicine

Divya Nagaraj  1 , Priya Khandelwal  1 , Sandra Steyaert  2 , Olivier Gevaert  3
Affiliations

Augmenting digital twins with federated learning in medicine

Divya Nagaraj et al. Lancet Digit Health. 2023 May.

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.

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Conflict of interest statement

OG reports grants to his institution from the US National Cancer Institute, AstraZeneca, National AI Center of Saudi Arabia, Owkin, Onc.AI, and Roche Molecular Systems. OG is named the inventor on a submission by Stanford University of a provisional patent “RNA to image synthetic data generator” 63/387,261 and a patent “Methods and systems for learning gene regulatory networks using sparse gaussian mixture models” PCT/US2022/080366. Other authors declare no competing interests.

Figures

Figure 1:
Figure 1:
Digital twins (DTs) can contain data from a variety of modalities such as notes, imaging, and wearables (Panel A). This vast amount of data can be processed with federated learning by just sharing model weights (Panel B) and could potentially be used to inform treatment options (for instance, lines of therapy for oncology).
Figure 1:
Figure 1:
Digital twins (DTs) can contain data from a variety of modalities such as notes, imaging, and wearables (Panel A). This vast amount of data can be processed with federated learning by just sharing model weights (Panel B) and could potentially be used to inform treatment options (for instance, lines of therapy for oncology).
See this image and copyright information in PMC

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