Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic
potential and poor prognosis, and has limited treatment options. The current standard of …
potential and poor prognosis, and has limited treatment options. The current standard of …
Advances in artificial intelligence for infectious-disease surveillance
Advances in Artificial Intelligence for Infectious-Disease Surveillance | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Auditing privacy defenses in federated learning via generative gradient leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems
by allowing multiple clients to participate in a learning task under the coordination of a …
by allowing multiple clients to participate in a learning task under the coordination of a …
Review on security of federated learning and its application in healthcare
Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …
progress has been made on many complex medical problems. However, there is a lack of …
Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions
Recent technological advancements have considerably improved healthcare systems to
provide various intelligent services, improving life quality. The Metaverse, often described as …
provide various intelligent services, improving life quality. The Metaverse, often described as …
Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models
Healthcare systems worldwide generate vast amounts of data from many different sources.
Although of high complexity for a human being, it is essential to determine the patterns and …
Although of high complexity for a human being, it is essential to determine the patterns and …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Specificity-preserving federated learning for MR image reconstruction
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …