Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …

Considerable political, structural, environmental and epidemiological change will affect high
socioeconomic index (SDI) countries over the next 25 years. These changes will impact …

Abstract Objective Federated Learning (FL) enables collaborative model training while
keeping data locally. Currently, most FL studies in radiology are conducted in simulated …

Mental health diagnostics is undergoing a transformation, with a shift away from traditional
categorical systems like the Diagnostic and Statistical Manual of Mental Disorders, Fifth …

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms
that can effectively work with decentralized data sources (eg, hospitals) without acquiring …

The integration of Artificial Intelligence (AI) into the field of emergency medicine has ushered
in a transformative era in healthcare. This narrative review provides a comprehensive …

The emerging technologies are revolutionizing predictive analytics in HealthCare domain.
The research in this sector has made great strides in managing disease management, which …

Background Approximately 300,000 patients per year are admitted to ICU in Canada
(average mortality: 10%). Even without a public health emergency, ICU beds are scarce and …

This paper describes a digital infrastructure designed to support AI research in medical
imaging, with a focus on Research Data Management best practices. The platform …