The adoption of machine learning approaches in systematic reviews is fundamentally transforming evidence-based medicine. Traditionally, systematic reviews have involved painstaking manual screening of ...

Given the success of the Digital Medicine and Chronic Neurological Disorders, we are pleased to announce Volume II.Digital medicine is the clinical part of ...

How people with compromised immune systems respond to vaccines is an important area of immunological research. A study led by York University has found that not only could machine-learning models ...

In the study The Shadow and the Self in Digital Twins in Healthcare as an AI Environment, published in AI & Society, researchers explore how digital twins may influence not only medical ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

From wearables for health monitoring and self-care apps, to machine learning analysis of medical images, the potential of digital technologies to revolutionise healthcare has commanded many headlines.

Metabolite data and AI combine to redefine how we measure aging and predict health spans. Study: Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning ...

Machine-learning models accurately pinpointed differences in immune responses in healthy controls and those living with HIV.

Researchers at Rice University and Baylor College of Medicine have developed a method that pairs engineered nanoparticles with machine learning algorithms to detect trace toxic pollutants in complex ...