More than 105 exhibitors presented AI-specific wares in the virtual AI showcase. That was down from 2019’s pre-COVID 150 but still easily beat 2018’s head count, around 75.

Only two of 156 scientific papers on AI published over the past 12 years used emotion as input data, according to a new literature review. And the reviewers focused on a specialty ripe for augmentation by artificial emotional intelligence: pregnancy health.

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.

The COVID crisis has given AI a chance to shine for medical researchers this year—and shine it has, judging by one prominent center’s experience.

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.

North of the border, two grants of $200,000 are on offer for researchers innovating transformative ways to apply AI in healthcare—and they’re only one part of an ambitious academic endeavor to expand medical AI.

Psychological researchers have used AI to not only test a hypothesis on COVID-related behaviors but also to help come up with the hypothesis in the first place.

Examples of bias are accumulating into an unignorable chink in healthcare AI’s armor.

Both outcomes and costs may vary widely from one patient to the next even in hospitals with consistently strong quality scores. Machine learning can help optimize the selection process for each patient as an individual.

A new AI tool can accurately separate the patients who would do better getting restorative sleep till morning from those who, due to unstable condition, really need their vital signs checked overnight.