As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and ...

The study demonstrates machine learning's role in predicting compressive strength of rice husk ash concrete, aiding the shift ...

There are more candidates on the waitlist for a liver transplant than there are available organs, yet about half the time a match is found with a donor who dies after cardiac arrest following the ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...

AI-enabled wearables are transforming preventive health by detecting abnormal heart rhythms, seizure-risk patterns and stress ...

Using this method, researchers were able to push the ability to find molecular traces in much older rocks. The state of the ...

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...

Introduction Atrial fibrosis identified on cardiac magnetic resonance (CMR) imaging has been proposed as a preprocedural ...

Tools Market reached a valuation of USD 150 billion in 2024, reflecting its rapid integration across global industries. Supported by accelerating AI adoption in enterprises, cloud ecosystems, and data ...

The hybrid model is emerging as the framework for trustworthy AI in test analytics. It retains traceability and supports continuous learning without losing control of causality. For engineers, that’s ...