As healthcare operators continually rely on medical devices in workflows, unsupported devices are likely to become more vulnerable to cyberattacks.

Academic and popular writings on the use of “embodied” AI in mental healthcare are piling up fast. But where’s the guidance for psychiatrists, psychotherapists and clinical social workers looking to use robots, avatars and chatbots with real patients?

Instead of Googling symptoms to when feeling an ailment and landing on an incorrect diagnosis, AI could soon provide accurate diagnosis without needing to go to a doctor’s office.

AI can speed up precise detection of one of the key signs of Alzheimer’s disease, according to researchers from University of California Davis and UC San Francisco, who published a study on their machine learning tool in Nature Communications.

Civica Rx, a not-for-profit drug company startup that aims to provide more affordable drugs, inked a new deal to distribute antibiotics with Xellia Pharmaceuticals, a specialty pharmaceutical company based in Copenhagen that develops, manufactures and commercializes anti-infective treatments.

Radiology, the medical specialty into which AI has made the furthest initial inroads in the U.S., is embracing the technology in France. And this is so despite French radiologists feeling underinformed on AI up to now.

Some are calling the first generation whose members will never have known life without smartphones “Generation Alpha.” And some are predicting they’ll be as reliant on AI as Millennials and Generation Z have been on the internet.

The simultaneous advances of deep learning and radiomics may soon yield a single unified framework for clinical decision support that has the potential to “completely revolutionize the field of precision medicine.”