We laud the authors of a recent CMAJ article for their usable framework for the development
and adoption of machinelearned solutions1 and propose that this will be useful to guide the …

In the article, the authors discuss the use and keys to future success of machine learning
(ML) in emergency medicine. Also cited are some flaws of ML like lack of interventional trial …

Artificial intelligence has been successfully applied to numerous health care and non‐health
care‐related applications and its use in emergency medicine has been expanding. Among …

Aim The emergence and evolution of artificial intelligence (AI) has generated increasing
interest in machine learning applications for health care. Specifically, researchers are …

Introduction The closest emergency department (ED) may not always be the optimal hospital
for certain stable high acuity patients if further distanced ED's can provide specialized care …

Clinical decision support systems powered by machine learning (ML) concepts have been a
long pursuit of future-oriented practitioners, patient safety experts, data scientists, and …

We recently brought an internally developed machine-learning model for predicting which
patients in the emergency department would require hospital admission into the live …

Objective Artificial intelligence (AI) has gradually found its way into healthcare, and its future
integration into clinical practice is inevitable. In the present study, we evaluate the accuracy …

As machine learning (ML) and precision medicine become more readily available and used
in practice, emergency physicians must understand the potential advantages and limitations …

Abstract Machine learning for hospital operations is under-studied. We present a prediction
pipeline that uses live electronic health-records for patients in a UK teaching hospital's …