Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may ...

Researchers from Odisha and Saudi Arabia have developed a hybrid AI model achieving 95.49% accuracy in predicting liver disease. This innovative tool, combining deep learning and boosting, promises ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

AI catches connections we miss. AI-IR was trained using independent cohorts from the United States and Taiwan, a collection of anonymized medical data ...

Insulin resistance—when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels—is ...

Tu, H. and Huang, Y.Y. (2026) Progress in Quantitative Imaging Assessment of Dermatomyositis-Associated Interstitial Lung ...

Heart specialists at Mayo Clinic today presented new research at the 2026 Society of Thoracic Surgeons Annual Meeting that ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...

There is an emerging convergence between atherosclerotic cardiovascular disease and cancer, driven by shared risk factors and overlapping pathophysiologic mechanisms. Traditional factors, such as ...

BrainIAC, a breakthrough AI foundation model, is able to predict brain age, dementia, time-to-stroke, and brain cancer from brain magnetic resonance imaging (MRI).

A team from the Faculty of Medicine and Health Sciences and the Institute of Neurosciences at the University of Barcelona (UBneuro) has applied advanced artificial intelligence techniques to better ...