IBM is moving forward with a $2 billion expansion in New York to develop a new AI center, according to a report published in The Washington Post.

A recent win by DeepMind, an AI lab owned by Google parent company Alphabet, in a protein-folding problem competition has scientists wondering how the technology can change the drug discovery industry, according to a report by The New York Times.

More than a dozen healthcare AI companies—including Owkin and Arterys—have been named as top startups in CB Insight’s third annual AI 100 list.

An AI algorithm helped determine adult women’s brains are, on average, a few years younger than the brains of males of the same age, according to research published in the Proceedings of the National Academy of Sciences.

The rise of AI and machine learning in the healthcare market hasn’t gone unnoticed by KenSci, a Seattle-based company that offers an AI and machine learning platform for healthcare groups. The company has now raised an additional $22 million in hopes of growing along with the market.

Utah-based healthcare big data company Health Catalyst announced it has closed $100 million in Series F equity and debt funding. Financing will support the expansion of Health Catalyst’s technology and services offering for healthcare information. 

It was a record-breaking year for medical device approvals in the FDA, with the government agency approving 106 novel devices in 2018—beating 2017’s 99 novel device approvals.

Reuters reports the Trump administration will take executive action in the next few weeks to ensure the U.S. keeps abreast of research and development opportunities in “industries of the future”—including AI and 5G networks.

Medicus AI GmbH, a Dubai-based technology company that uses AI to make health data more understandable, has raised about $3.1 million in a Series A funding round, according to information available on Crunchbase.

Northwell Health, a New York-based healthcare network, is integrating predictive AI software into the electronic medical records (EMRs) at 15 of its hospitals to identify patients at risk of being readmitted to the hospital.

A deep learning model classified acute and nonacute pediatric elbow abnormalities on radiographs in trauma with 88 percent accuracy, according to new research published in Radiology: Artificial Intelligence

Machine-learning models could be used to help improve the prediction ability of emergency room (ER) triage methods, after a JAMA study showed the technology was better at making clinical predictions than traditional approaches.