Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …

Abstract Trustworthy Artificial Intelligence (AI) is based on seven technical requirements
sustained over three main pillars that should be met throughout the system's entire life cycle …

Advances in artificial intelligence (AI) are enabling systems that augment and collaborate
with humans to perform simple, mechanistic tasks such as scheduling meetings and …

Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …

This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …

Undetected infection and delayed isolation of infected individuals are key factors driving the
monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of …

To the Editor—In medicine, digital twin models use real-time data to adjust treatment,
monitor response and track lifestyle modifications. Similarly, cancer patient digital twins …

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack
of objective outcomes and fidelity metrics. AI technologies and specifically Natural …

Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …