Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday
life activities. The implementation of AI algorithms in clinical practice has been met with …

There is a growing awareness that artificial intelligence (AI) has been used in the analysis of
complicated and big data to provide outputs without human input in various health care …

Objective To create and validate a simple and transferable machine learning model from
electronic health record data to accurately predict clinical deterioration in patients with covid …

Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …

Abstract Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the
healthcare sector. This review considers twelve key aspects of AI in clinical practice: 1) …

Objective Clinical prediction models require updating as performance deteriorates over time.
We developed a testing procedure to select updating methods that minimizes overfitting …

Background Data drift can negatively impact the performance of machine learning
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …

Artificial intelligence (AI) in health care is the future that is already here. Despite its potential
as a transformational force for primary care, most primary care providers (PCPs) do not know …

The need for AI systems to provide explanations for their behaviour is now widely
recognised as key to their adoption. In this article, we examine the problem of trustworthy AI …

A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …