A machine learning–driven web tool based on 13 standard patient metrics demonstrates strong predictive performance for MASLD, ...

Heyjun Park of the Johns Hopkins Bloomberg School of Public Health identifies biological and behavioral markers linked to heart disease, diabetes and pregnancy outcomes. She will speak Wednesday.

Imagine a future where Artificial Intelligence (AI) can forecast medical conditions years before any symptoms appear. What ...

An AI system that can predict what a patient's knee X-ray will look like a year in the future could transform how millions of ...

The drug development pipeline is a costly and lengthy process. Identifying high-quality "hit" compounds—those with high ...

Children living near e-waste dumps face a fourfold higher risk of hypertension due to toxic metal and chemical exposure, ...

A USC-led team has developed an innovative way to predict blood pressure outcomes after bariatric surgery that outperforms ...

USC researchers used metabolomics and proteomics to predict which adolescents would see long-term blood pressure improvements ...

There’s a need in secondary prevention for personalized risk estimates to motivate patients and guide care, researchers say.

When machine learning is used to suggest new potential scientific insights or directions, algorithms sometimes offer ...

Researchers developed seven MRI-based biological age clocks across major organs using UK Biobank imaging, linking each to ...

Cardiologists often struggle to assess heart attack risk. New startups using AI could help.