Besides building analytical solutions for a large-scale organization, Sree Hari Subhash, an international IT expert, has ...

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a ...

This imbalance has contributed to what has been referred to as the “artificial intelligence chasm,” representing the gap between the development and validation of machine-learned algorithms and their ...

AEquity aims to improve training for healthcare machine-learning algorithms by assessing the accuracy and fairness of the data they're fed.

The symbiotic relationship between machine learning and chemistry holds the promise of unlocking insights and accelerating ...

Obermeyer said in the interview that in machine learning, machines aren’t given rules about computing data; rather, they learn the rules after taking in data and create algorithms.

Machine learning, experts say, stands to empower doctors and benefit patients. But how will both respond to the idea of algorithms playing a bigger role in the medical system?

In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. These data are collected and ...