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

• Main Authors: Teo, Zhen Ling, Jin, Liyuan, Li, Siqi, Miao, Di, Zhang, Xiaoman, Ng, Wei Yan, Tan, Ting Fang, Lee, Deborah Meixuan, Chua, Kai Jie, Heng, John, Liu, Yong, Goh, Rick Siow Mong, Ting, Daniel Shu Wei
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Artificial intelligence; Federated learning
• Online Access: https://hdl.handle.net/10356/178493
• Institution: Nanyang Technological University

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.