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 …

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly
driven by the emergence of increasingly accurate machine learning models. However, the …

Background Despite the expanding use of machine learning (ML) in fields such as finance
and marketing, its application in the daily practice of clinical medicine is almost non-existent …

Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making
and revolutionizing the field of health care. A major open challenge that AI will need to …

Abstract Machine learning (ML) and its parent technology trend, artificial intelligence (AI),
are deriving novel insights from ever larger and more complex datasets. Efficient and …

Artificial intelligence (AI) has been the subject of considerable interest for many years for its
potential to improve clinical care—yet its actual impact on patient outcomes when deployed …

Abstract Machine learning technologies have seen increased application to the healthcare
domain. The main drivers are openly available healthcare datasets, and a general interest …

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …

Healthcare systems are currently adapting to digital technologies, producing large quantities
of novel data. Based on these data, machine-learning algorithms have been developed to …