Federated Learning in Health care Using Structured Medical Data.

Area of Science:

• Machine Learning
• Medical Informatics
• Health Data Science

Background:

• Machine learning success relies on large datasets, often unavailable within single institutions.
• Multicenter studies are crucial for data volume but face legal, business, and technical data-sharing hurdles.
• Existing data-sharing methods present significant challenges for collaborative medical research.

Purpose of the Study:

• To review recent studies using federated learning (FL) in clinical settings with structured medical data.
• To explore the potential of FL in overcoming data access limitations in multicenter health studies.
• To discuss current challenges and future research directions for FL in clinical data analysis.

Main Methods:

• Systematic review of published literature on federated learning in clinical studies.
• Analysis of studies utilizing structured medical data within a federated learning framework.
• Identification and discussion of common themes, challenges, and outcomes in applied FL.

Main Results:

• Federated learning effectively facilitates multicenter studies by enabling local model training and global model aggregation.
• FL addresses data access issues while maintaining data control and privacy across participating institutions.
• Recent studies demonstrate the feasibility and benefits of FL for structured clinical data.

Conclusions:

• Federated learning offers a promising solution for collaborative machine learning in healthcare, overcoming traditional data-sharing barriers.
• Further research is needed to address the challenges and optimize FL implementation in diverse clinical settings.
• FL supports the development of robust global models from distributed health data, advancing medical research.