Federated Learning in Medical Imaging: Enhancing Data Privacy and Advancing Healthcare

Federated learning is revolutionizing medical imaging by enabling collaborative AI development while preserving data privacy. In this session, attendees will gain insights into how federated learning advances healthcare by protecting patient data, simplifying collaboration, and accelerating the development of AI solutions for medical imaging. In healthcare, sharing sensitive patient data across institutions is challenging due to privacy regulations and contracting issues. Federated Learning addresses these concerns by allowing institutions to train AI models on decentralized data without transferring raw patient information. This session will explore how federated learning overcomes traditional barriers to innovation, with a focus on practical applications that enable healthcare organizations to harness distributed computing power. We will highlight key use cases, such as handling rare diseases with limited data availability across institutions and overcoming the lack of centralized compute resources often needed for training complex AI models. We will also examine the impact of federated learning on real-world case studies that not only tackle data privacy concerns, but also support researching rare disease and triaging patients.