This article presents a mapping review of the literature concerning the ethics of artificial
intelligence (AI) in health care. The goal of this review is to summarise current debates and …

Healthcare systems across the globe are struggling with increasing costs and worsening
outcomes. This presents those responsible for overseeing healthcare with a challenge …

In this paper, we address the ice-start problem, ie, the challenge of deploying machine
learning models when only a little or no training data is initially available, and acquiring …

Predicting the toxicity of a compound preclinically enables better decision making, thereby
reducing development costs and increasing patient safety. It is a complex issue, but in vitro …

Cyberattacks from within an organization's trusted entities are known as insider threats.
Anomaly detection using deep learning requires comprehensive data, but insider threat data …

A salient approach to interpretable machine learning is to restrict modeling to simple
models. In the Bayesian framework, this can be pursued by restricting the model structure …

Heart arrhythmia have been a life-threatening disease to human for a very long time, many
techniques and methods have been developed by human experts since the advent of ECG …

To design a device that is robust to process-induced random variation, this study proposes a
machine-learning-based predictive model that can simulate the electrical characteristics of …

This article studies the infinite-width limit of deep feedforward neural networks whose
weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden …

In the modern world, machine learning, including deep learning, has become an
indispensable part of many intelligent systems, helping people automate the decision …