Semantic Scholar Open Access 2020 2563 sitasi

The future of digital health with federated learning

Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletarì H. Roth +12 lainnya

Lihat Sumber DOI

Abstrak

Data-driven machine learning (ML) 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.

Topik & Kata Kunci

Computer Science Medicine

Penulis (17)

Nicola Rieke

Jonny Hancox

Wenqi Li

Fausto Milletarì

H. Roth

Shadi Albarqouni

S. Bakas

M. Galtier

B. Landman

Klaus H. Maier-Hein

S. Ourselin

Micah J. Sheller

Ronald M. Summers

Andrew Trask

Daguang Xu

Maximilian Baust

M. Cardoso

Format Sitasi

APA MLA BibTeX

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H., Albarqouni, S. et al. (2020). The future of digital health with federated learning. https://doi.org/10.1038/s41746-020-00323-1

Akses Cepat

Lihat di Sumber doi.org/10.1038/s41746-020-00323-1

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 2563×
Sumber Database: Semantic Scholar
DOI: 10.1038/s41746-020-00323-1
Akses: Open Access ✓