The Operationalization Gap. It’s the chasm between innovation and everyday practice that is a challenge for all large organizations. Hospital executives will likely not be surprised to learn that the ...

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...

Big data, machine learning, and interoperability are all topics we’ve been hearing about for many years in health tech. But in fact these banner ideas are deeply intertwined with one another. Machine ...

Increasing surgical services revenue is a top priority for most health systems, but reliance on manual operating room scheduling and operational inefficiencies can impede these efforts. Often, poor ...

Clinical and operational machine learning models are gaining ground at hospitals and health systems throughout the country, and new ones are evolving rapidly. But at this point, the challenge is not ...

This week on “Podnosis,” we explore the pressing questions surrounding AI’s role in healthcare. From clinician trust to algorithm accuracy and patient privacy, there’s significant uncertainty about AI ...

Using infrared light and machine learning, researchers have developed a method to effectively screen human health and its deviations at a population level. Envision a scenario where a single drop 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 ...

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 ...

If you’ve ever had a doctor order a blood test for you, chances are that they ran a complete blood count or CBC. One of the most common blood tests in the world, CBC tests are run billions of times ...