With the development of information technology, artificial intelligence shows great potentials
for clinical diagnosis and treatment. Nevertheless, bias in algorithms derived by artificial …

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

Artificial intelligence (AI) is widely used in medical applications to support outcome
prediction and treatment optimisation based on collected patient data. With the increasing …

Artificial intelligence (AI) algorithms have become increasingly prevalent in healthcare,
revolutionizing various aspects of patient care, diagnosis, and treatment. These algorithms …

Background: With the increasing digitization and growing integration of Artificial Intelligence
(AI) in healthcare (eg for diagnosis), the risk of incorporating unfair and biased behaviours in …

Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence
(AI) into the healthcare field the future care of our patients will depend on the decisions we …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

The field of artificial intelligence (AI) has evolved considerably since the end of the 20th
century. While this technology shows great promise and potential to solve daily tasks, the …

1.1. Objectives Biases inherent in electronic health records (EHRs), and therefore in medical
artificial intelligence (AI) models may significantly exacerbate health inequities and …

In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …