Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …

Federated learning allows distributed medical institutions to collaboratively learn a shared
prediction model with privacy protection. While at clinical deployment, the models trained in …

Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …

Federated learning (FL) is a method used for training artificial intelligence models with data
from multiple sources while maintaining data anonymity, thus removing many barriers to …

Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …

Federated learning is an emerging research paradigm enabling collaborative training of
machine learning models among different organizations while keeping data private at each …

Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …