The rising popularity of artificial intelligence in healthcare is highlighting the problem that a
computational model achieving super-human clinical performance at its training sites may …

The rising popularity of artificial intelligence in healthcare is highlighting the problem that a
computational model achieving super-human clinical performance at its training sites may …

Advances in medical machine learning are expected to help personalize care, improve
outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to …

In many fields, including medicine and biology, there has been in the last years an
increasing diffusion and availability of complex data from different sources. Examples …

Background Substantial effort has been directed towards demonstrating use cases of
Artificial Intelligence in healthcare, yet limited evidence exists about the long-term viability …

For machine learning developers, the use of prediction tools in real-world clinical settings
can be a distant goal. Recently published guidelines for reporting clinical research that …

Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …

This paper considers the use of machine learning in medicine by focusing on the main
problem that it has been aimed at solving or at least minimizing: uncertainty. However, we …

Few published data science tools are ever translated from academia to real-world clinical
settings for which they were intended. One dimension of this problem is the software …

To improve the performance of medical practice and the impact of biomedical research on
health treatment and policy making, a new way of looking at complex diseases is arising …