Future of the Medical World: Collaborative Medical Imaging AI With Federated Learning

Abstract

Integrating federated learning with medical imaging represents a significant development in the rapidly evolving field of healthcare technology. Federated learning can enable medical institutions to train AI models collaboratively while preserving patient privacy by keeping data localized, addressing regulatory, and ethical concerns associated with centralized data sharing. Moreover, federated learning can affect a shift from hospital-centric care to proactive and continuous care in everyday settings by ensuring real-time data-driven support beyond the clinical environment. In particular, federated learning can take advantage of new consumer technologies by aggregating data from wearable health devices, smart home monitoring systems, and patient-centric tools, empowering individuals to manage their health actively. However, while federated learning shows great promise in improving diagnostics, fostering research collaborations, and facilitating continuous care, it also faces challenges, such as data heterogeneity and model aggregation complexity. As healthcare technology evolves toward a future where privacy, collaboration, and personalization coexist, federated learning has emerged as a critical framework for driving ethically responsible and innovative medical practices. This review explores the transformative potential of federated learning, including its methodologies, associated challenges, and future implications.

Ähnliche Arbeiten