The metaverse integrates physical and virtual realities, enabling humans and their avatars to
interact in an environment supported by technologies such as high-speed internet, virtual …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

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 …

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 …

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 …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Abstract Machine learning (ML) and deep learning (DL) are both branches of AI. As a form of
AI, ML automatically adapts to changing datasets with minimal human interference. Deep …

Institutions in highly regulated domains such as finance and healthcare often have restrictive
rules around data sharing. Federated learning is a distributed learning framework that …

Federated learning (FL) allows the collaborative training of AI models without needing to
share raw data. This capability makes it especially interesting for healthcare applications …

The current prognostics approaches for a network of assets are centralized and reliant on
the availability of assets' sensors, failures, and anomaly data. To address this, the data from …