Federated learning for medical imaging radiology

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...

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Bibliographic Details
Published in:British journal of radiology 2023-10, Vol.96 (1150), p.20220890-20220890
Main Authors: Rehman, Muhammad Habib ur, Hugo Lopez Pinaya, Walter, Nachev, Parashkev, Teo, James T, Ourselin, Sebastin, Cardoso, M Jorge
Format: Article
Language:English
Subjects:
AI in imaging and therapy: innovations, ethics and impact: Review
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Summary: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.
ISSN:0007-1285
1748-880X
DOI:10.1259/bjr.20220890