In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Cardiovascular disease is the leading contributor to years lost due to disability or premature
death among adults. Current efforts focus on risk prediction and risk factor mitigation ‚which …

As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

Given the ubiquity of deep neural networks, it is important that these models do not reveal
information about sensitive data that they have been trained on. In model inversion attacks …

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

Komisję ESC ds. Wytycznych Postępowania nadzoruje i koordynuje przygotowywanie
nowych wytycznych i stanowisk przez grupy robocze i inne grupy ekspertów. Komitet jest …

There is great potential for genome sequencing to enhance patient care through improved
diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential …

Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …

This document is an update to the 2011 Clinical Pharmacogenetics Implementation
Consortium (CPIC) guideline for CYP2C9 and VKORC1 genotypes and warfarin dosing …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …