Leaders of the venture, dubbed “Project Amber,” are open-sourcing their technology and findings in the hope that other mental health researchers can take things from here.

The bytes are accumulating far too fast for any human effort to connect whatever dots might be hiding in the massive muddle. Enter 40 researchers at 11 sites armed with AI and almost $18 million in new NIH funding.

Analyzing the scientific literature on medical AI published over the past 46 years, researchers in the U.K. have found the U.S. far ahead of the field for sheer quantity.

Google Health all but invited the blowback when its AI developer-researchers suggested their breast-cancer model may be superior to radiologists’ eyes and generalizable across differing demographics.

If big data is to fulfill its potential for advancing the state of modern healthcare, developers of medical AI must be willing to show their work. 

Three machine learning algorithms have identified patients likely to suffer extreme pain following surgery with about 80% accuracy each.

Researchers at Columbia University have developed a machine learning algorithm that identifies and predicts gender-based differences in adverse reactions to drugs. 

A small but mighty research outfit based in Hungary has compiled a user-friendly database of medical technologies anchored in AI and approved by the FDA.

A multinational group of scholars has put together two fresh sets of guidelines for researchers testing AI applications in clinical trials.

Researchers in Canada are working to develop AI models for diagnosing and treating mental illness. One application in their sights involves automated interpretations of brain scans.