Administrative and medical processes of the healthcare organizations are rapidly changing
because of the use of artificial intelligence (AI) systems. This change demonstrates the …

Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

The use of machine learning (ML) with electronic health records (EHR) is growing in
popularity as a means to extract knowledge that can improve the decision-making process in …

Medical data widely exist in the hospital and personal life, usually across institutions and
regions. They have essential diagnostic value and therapeutic significance. The disclosure …

Background The outbreak of coronavirus disease 2019 (COVID-19) has globally strained
medical resources and caused significant mortality. Objective To develop and validate a …

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 …

Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a
machine learning algorithm without sharing their data. Organizations instead share model …

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 …

Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …