Federated learning for medical imaging radiology

Muhammad Habib Ur Rehman*, Walter Hugo Lopez Pinaya, Parashkev Nachev, James T. Teo, M. Jorge Cardoso

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)


Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions.

Original languageEnglish
Article number20220890
JournalBritish Journal of Radiology
Issue number1150
Publication statusPublished - Oct 2023


Dive into the research topics of 'Federated learning for medical imaging radiology'. Together they form a unique fingerprint.

Cite this