4004 ESC Guidelines label use of medication should be limited to situations where it is in
the patient's interest to do so, with regard to the quality, safety, and efficacy of care, and only …

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

It is widely hoped that statistical models can improve decision-making related to medical
treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically …

Develo** a single-domain assay to identify individuals at high risk of future events is a
priority for multi-disease and mortality prevention. By training a neural network, we …

External validation studies are an important but often neglected part of prediction model
research. In this article, the second in a series on model evaluation, Riley and colleagues …

Background We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19)
with multivariate analysis and establish a predictive model of disease progression to help …

Clinical prediction models are used frequently in clinical practice to identify patients who are
at risk of develo** an adverse outcome so that preventive measures can be initiated. A …

Background & Aims Immunotherapy with atezolizumab plus bevacizumab represents the
new standard of care in systemic front-line treatment of hepatocellular carcinoma (HCC) …

Background Clinical prediction models are often not evaluated properly in specific settings
or updated, for instance, with information from new markers. These key steps are needed …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …