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 …

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 …

Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

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 …

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 …

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 …

Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

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