Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …

Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

Purpose Due to the increasing demand for intensive care unit (ICU) treatment, and to
improve quality and efficiency of care, there is a need for adequate and efficient clinical …

Cancer is defined as a large group of diseases that is associated with abnormal cell growth,
uncontrollable cell division, and may tend to impinge on other tissues of the body by different …

The development and implementation of clinical decision support (CDS) that trains itself and
adapts its algorithms based on new data—here referred to as Adaptive CDS—present …

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical
artificial intelligence (AI). The number of applications of clinical AI is increasing, which …

There is much discussion concerning 'digital transformation'in healthcare and the potential
of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions …

Objective Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now
feasible for many health systems, yet little is known about effective strategies of system …

With the increasing availability of huge datasets and the scaling of computational power, the
use of artificial intelligence (AI) and machine learning (ML) algorithms is rapidly growing. We …

Neurodegenerative diseases (NDDs), which are chronic and progressive diseases, are a
growing health concern. Among the therapeutic methods, stem-cell-based therapy is an …