The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …

The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

The integration of artificial intelligence (AI) in medical image interpretation requires effective
collaboration between clinicians and AI algorithms. Although previous studies demonstrated …

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Background Timely interventions, such as antibiotics and intravenous fluids, have been
associated with reduced mortality in patients with sepsis. Artificial intelligence (AI) models …

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and
Blockchain technologies have quickly gained pace as a new study niche in numerous …

Translating machine learning (ML) models effectively to clinical practice requires
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple
devices collaboratively learn a machine learning model without sharing their private data …

High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is
required as soon as possible. Machine learning technique has become reliable for medical …