This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Aims and objectives To identify key areas of competence for digitalisation in healthcare
settings, describe healthcare professionals' competencies in these areas and identify factors …

Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …

The rapid growth of digital technologies and the extraordinary amount of data that devices
and applications collect each day are increasingly driving companies to radically transform …

Deep learning models for semantic segmentation of images require large amounts of data.
In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling …

Abstract Mobile health, or 'mHealth', is the application of mobile devices, their components
and related technologies to healthcare. It is already improving patients' access to treatment …

Big Data has recently emerged as a new digital paradigm, one that companies adopt in
order to both transform existing business models and nurture their innovation activities. The …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Background: Wearable sensing and information and communication technologies are key
enablers driving the transformation of health care delivery toward a new model of connected …

Background Machine learning (ML), a type of artificial intelligence (AI) technology that uses
a data-driven approach for pattern recognition, has been shown to be beneficial for many …