Only 19 of 159 radiation oncology professionals working across Canada, or 12%, feel they’re well-versed in AI. However, more than 90% are open to learning its ways.

The scientists focused on getting their AI to mimic the cortical mechanism of “gating,” which controls information flow between neuron clusters to apply existing knowledge to new situations.

Oxford researchers have developed and prospectively validated two AI tools that can quickly screen hospital patients for COVID-19 using routine clinical data.

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.

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.

Drawing on nothing more than Facebook activity, psychiatric AI can distinguish individuals headed for hospitalization with schizophrenia from those with worsening mood disorders such as clinical depression and bipolar states. 

A women’s health technology company has received the FDA’s blessing to market deep learning-based software designed to help breast radiologists spot hard-to-find nascent cancers in 3D mammograms.

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.