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

Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …

Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …

Health information technology can support the development of national learning health and
care systems, which can be defined as health and care systems that continuously use data …

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 …

Data-driven digital health technologies have the power to transform health care. If these
tools could be sustainably delivered at scale, they might have the potential to provide …

Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …

Methods Images from a public COVID-19 database were scored retrospectively by three
blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A …

Machine learning systems have received much attention recently for their ability to achieve
expert-level performance on clinical tasks, particularly in medical imaging. Here, we …

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in
practice is critical. Clinical evaluation aims to confirm acceptable AI performance through …