The application of artificial intelligence and machine learning (ML) technologies in
healthcare have immense potential to improve the care of patients. While there are some …

The increasing implementation of and reliance on machine-learning (ML) algorithms to
perform tasks, deliver services and make decisions in health and healthcare have made 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 …

The application of machine-learning technologies to medical practice promises to enhance
the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of …

Along with potential benefits to healthcare delivery, machine learning healthcare
applications (ML-HCAs) raise a number of ethical concerns. Ethical evaluations of ML-HCAs …

Objectives Fairness is a core concept meant to grapple with different forms of discrimination
and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet …

INTRODUCTION Machine learning for healthcare (MLHC) is at the juncture of leaping from
the pages of journals and conference proceedings to clinical implementation at the bedside …

Machine learning (ML) approaches have demonstrated promising results in a wide range of
healthcare applications. Data plays a crucial role in developing ML-based healthcare …

The panel aims to elucidate how different national govenmental programs are implementing
accountability of machine learning systems in healthcare and how accountability is …

Artificial intelligence has exposed pernicious bias within health data that constitutes
substantial ethical threat to the use of machine learning in medicine. 1, 2 Solutions of …