Disease modeling and target identification are the most crucial initial steps in drug
discovery, and influence the probability of success at every step of drug development …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Objective: Clinical applications of machine learning are promising as a tool to improve
patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for …

Vision-language models are growing in popularity and public visibility to generate, edit, and
caption images at scale; but their outputs can perpetuate and amplify societal biases …

Recent research has revealed that many machine-learning models and the datasets they
are trained on suffer from various forms of bias, and a large number of different fairness …

This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

Objective: Clinical data's confidential nature often limits the development of machine
learning models in healthcare. Generative adversarial networks (GANs) can synthesise …

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems
(AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This …

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art
technologies engaging artificial intelligence. The unavailability of quality data due to both …