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. 2024 Feb 20;5(2):101419.
doi: 10.1016/j.xcrm.2024.101419. Epub 2024 Feb 9.

Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture

Zhen Ling Teo  1 , Liyuan Jin  2 , Siqi Li  2 , Di Miao  2 , Xiaoman Zhang  3 , Wei Yan Ng  1 , Ting Fang Tan  1 , Deborah Meixuan Lee  4 , Kai Jie Chua  1 , John Heng  1 , Yong Liu  5 , Rick Siow Mong Goh  5 , Daniel Shu Wei Ting  6
Affiliations

Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture

Zhen Ling Teo et al. Cell Rep Med. .

Erratum in

Abstract

Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles in scientific, engineering, and medical journals in English up to August 31st, 2023. Out of a total of 22,693 articles under review, 612 articles are included in the final analysis. The majority of articles are proof-of-concepts studies, and only 5.2% are studies with real-life application of FL. Radiology and internal medicine are the most common specialties involved in FL. FL is robust to a variety of machine learning models and data types, with neural networks and medical imaging being the most common, respectively. We highlight the need to address the barriers to clinical translation and to assess its real-world impact in this new digital data-driven healthcare scene.

Keywords: artificial intelligence; federated learning; healthcare; systematic review.

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Conflict of interest statement

Declaration of interests D.S.W.T. holds a patent on a deep learning system for detection of retinal diseases and co-founded and holds equity in EyRIS Singapore.

Figures

None
Graphical abstract
Figure 1
Figure 1
Federated learning in comparison to local learning and centralized learning
Figure 2
Figure 2
Summary of article selection process
Figure 3
Figure 3
Global map of federated learning health-related research
Figure 4
Figure 4
Trend of FL article by publication year ∗Up to August 31st, 2023.
Figure 5
Figure 5
Evidence map on the data variety used across article types
Figure 6
Figure 6
Evidence map on the distribution of article types across medical subspecialties
Figure 7
Figure 7
Technical architecture of studies
See this image and copyright information in PMC

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