Software that uses AI to make cardiac-ultrasound experts of healthcare workers with no prior ultrasound experience has received “Breakthrough Device” designation from the FDA.

Aiming to develop and market AI algorithms for diagnosing prostate cancer on MRI scans, IBM Watson Health is working with Guerbet, a France-based maker of contrast agents used in medical imaging.

A meta-analysis of 14 studies has shown AI algorithms correctly diagnose diseases in medical imaging around 87% of the time while ruling out specific diseases with 93% accuracy.

Kheiron Medical Technologies, a London-based machine learning startup focused on helping radiologists detect cancer at an early stage, has raised $22 million in a Series A funding round.

Deep-learning analysis of eye scans has proven superior to conventional analysis of the same images for the task of detecting and tracking vision diseases like glaucoma and age-related macular degeneration.

Portable ultrasound maker Fujifilm SonoSite has turned to the nonprofit Allen Institute for Artificial Intelligence’s AI2 Incubator for help harnessing AI’s image-interpretation potential.

An AI-based smartphone app for detecting cancer in skin lesions has proven quite capable, achieving 95.1% sensitivity.

Terason, a portable ultrasound manufacturer based in Massachusetts, is partnering with DiA Imaging Analysis, which is headquartered in Israel, to bring AI to healthcare providers using Terason machines for heart imaging.

Gadolinium-based contrast agents (GBCAs) can often bring out the best in MRI, but they’re controversial and thus increasingly avoided. A pilot study in Germany shows how an algorithm might substitute for an injection to track tumors of the brain and spinal cord (aka gliomas).

Aided by augmented reality, AI and portable neuroimaging technology, physicians may soon be able to tease out images of patients’ brains—right there in the doctor’s office—to see how much pain each patient is suffering.

Health officials south of the border may soon be able to fight a nasty disease using just their smartphones and an AI tool for reverse image searches.

Radiology is the medical specialty most conducive to clinical AI applications. After all, the pre-AI technique of computer-aided detection has been used in mammography since 1998, for example. So it shouldn’t come as a surprise to find AI “app stores” rising in radiology.