In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI),
including machine learning (ML) techniques have been deployed to predict, diagnose, and …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …

The AI life cycle: a holistic approach to creating ethical AI for health decisions | Nature Medicine
Skip to main content Thank you for visiting nature.com. You are using a browser version with …

Abstract Machine learning technologies have seen increased application to the healthcare
domain. The main drivers are openly available healthcare datasets, and a general interest …

A clinical sentiment is a judgment, thought or attitude promoted by an observation with
respect to the health of an individual. Sentiment analysis has drawn attention in the …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Critical care data reflect the most physiologically unstable patients in a hospital. These
patients are heavily monitored and may undergo complex treatment regimens to manage …

Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …

Artificial Intelligence and Learning Futures: Critical Narratives of Technology and
Imagination in Higher Education explores the implications of artificial intelligence's adoption …