Edge Devices for personalized and privacy preserving patient care

The medical field is shifting from generic approaches to personalized, data-driven, risk-based care. Leveraging federated learning and AI, our project aims to design, develop, and deploy trustworthy and privacy-preserving algorithms that assist clinicians, patients, and caregivers in managing both acute and chronic phases of ischemic stroke.
Building on CERN’s CAFEIN Federated Learning Platform (link placeholder), this initiative brings AI closer to the patient by deploying models directly on edge devices, ensuring maximum privacy and data control.