[Promoting the application of federated learning in medical imaging artificial intelligence].

Abstract

Medical image-based artificial intelligence (AI) systems have shown great potential in assisting disease diagnosis and treatment. However, the challenges, such as data silos, privacy security and standardization, seriously impedes the application of AI in disease diagnosis and treatment. By integrating federated learning technology and FAIR data principle, it is possible to resolve the aforementioned obstacles. Then, it is able to maximize the value of multicenter data to develop a more efficient and accurate disease diagnosis and treatment AI systems, and promote the clinical application of medical image-based AI systems in the field of disease diagnosis and treatment.

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