Information technology and connectivity have transformed productivity and costs in nearly every industry. Health care, however, has remained persistently immune to this transmogrification. Electronic health records (EHRs) have been particularly disappointing on this front, with time-consuming and inconsistent physician data entry as well as poor integration across complex and emerging data sources from medical devices, imaging, genomics and wearables and, as a consequence, a lack of usefulness in improving population health analytics or personalized care.

Deep learning algorithms developed at the Memorial Sloan Kettering Cancer Center (MSK) were able to distinguish prostate, skin and breast cancer with nearly perfect accuracy in a recent clinical trial. The technology has already been licensed exclusively by New York-based startup Paige.AI, which snapped up a $25 million series A early last year to continue to advance it.

With the ongoing push toward value-based care, providers are looking for ways to improve patient outcomes while also lowering health care costs. Los Angeles-based Dearhealth Inc.'s artificial intelligence-powered software-as-a-service (SaaS) platform aims to do meet that demand by helping physicians better manage patients with chronic conditions. Now Philips Health Technology Ventures and other large investors are putting their money behind the company, seeing an opportunity to generate real movement in advance population health.

A fast response with cardiopulmonary resuscitation (CPR) for cardiac arrest victims can save their lives, but older adults often are alone in their home or a bedroom when symptoms strike. Researchers at the University of Washington (UW) have developed a machine learning-based system that listens to ambient audio via dedicated smart speakers or smartphones for agonal breathing, the distinctive sound that a person in cardiac arrest makes.