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 …

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote
technological solutions. Rising rates of life expectancy of older people, and deaths due to …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

Predictive artificial intelligence (AI) systems based on deep learning have been shown to
achieve expert-level identification of diseases in multiple medical imaging settings, but can …

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …

Several sources of bias can affect the performance of machine learning systems used in
medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias …

As AI models continue to advance into many real-life applications, their ability to maintain
reliable quality over time becomes increasingly important. The principal challenge in this …

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems
requires the ability to interrogate data and models at all stages of the development pipeline …

BACKGROUND: Artificial intelligence (AI) applications aiming to support surgical decision-
making processes are generating novel threats to ethical surgical care. To understand and …

Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and
processing health data is essential for the new diagnostic and decisional technologies but …