Advances in mechanistic modeling, machine learning, and biomedical data integration are making it possible to move beyond “one-size-fits-all” evidence and ...

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

Cardiometabolic syndrome arises from intricate interactions among metabolic, cardiovascular, behavioral, and environmental factors. The convergence of ...

Digital twins revolutionize drug discovery by integrating AI and biological data, enhancing prediction, trial design, and ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...