Researchers have demonstrated a machine learning model that can rule out COVID-19 in many emergency patients—and all the algorithm needs is data from routine ER blood tests.

The novel coronavirus has shown a nasty penchant for targeting the kidneys, and physicians can’t always tell which patients will need dialysis until they do. By then it’s often too late to save a life.

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

The healthcare AI startup founded by developers of the Apple Watch has been greenlit to market an AI-powered mobile application that lets radiologists interpret images and share the results with patients in real time.

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.

Various scenarios within medical diagnostics are among the AI use cases that an official European watchdog has flagged as a potential source of hazards to fundamental human rights.

Parkinson’s disease is diagnosable early with a basic eye exam and AI, according to researchers at the University of Florida.

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.

When it comes to predicting an individual’s risk of suffering a heart attack or stroke over the next 10 years, machine learning models are no more consistent than traditional statistical techniques.