Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
@article{arxiv.2003.08119,
title = {The Future of Digital Health with Federated Learning},
author = {Nicola Rieke and Jonny Hancox and Wenqi Li and Fausto Milletari and Holger Roth and Shadi Albarqouni and Spyridon Bakas and Mathieu N. Galtier and Bennett Landman and Klaus Maier-Hein and Sebastien Ourselin and Micah Sheller and Ronald M. Summers and Andrew Trask and Daguang Xu and Maximilian Baust and M. Jorge Cardoso},
journal= {arXiv preprint arXiv:2003.08119},
year = {2021}
}
Comments
This is a pre-print version of https://www.nature.com/articles/s41746-020-00323-1