As an integrated health-delivery network comprising 13 hospital campuses, two research centers and a health plan with more than half a million subscribers sitting atop the biggest biobank with whole exome (DNA) sequence data in existence, Pennsylvania’s Geisinger Health System is one of the best-positioned institutions in the U.S. to explore the possibilities and initial successes of AI in healthcare. The institution is bringing complex algorithmic concepts to everyday patient care and showing others the path forward.

Artificial and augmented intelligence are driving the future of medical imaging. Tectonic is the only way to describe the trend. And medical imaging is at the right place at the right time. Imaging stands to get better, stronger, faster and more efficient thanks to artificial intelligence, including machine learning, deep learning, convolutional neural networks and natural language processing. So why is medical imaging ripe for AI? Check out the opportunities and hear what experts have to say—and see what you should be doing now if you haven’t already started.

A multi-institutional team of researchers has developed a new AI learning algorithm that can distinguish between low- and high-risk prostate cancer from multiparametric MRI (mpMRI) scans with higher sensitivity and predictive value than current risk assessment approaches, according to research published online Feb. 7 in the journal Scientific Reports.

Researchers have developed a new framework that uses machine learning to predict prostate cancer progression, according to new findings published in Scientific Reports.

Microsoft unveiled a new tool that allows healthcare organizations to create their own AI-powered chatbots and virtual assistants for various services.

An international group of researchers found evidence of unique patterns of brain activity that may explain the neurological difference between consciousness and unconsciousness, according to a Feb. 6 study published in Science Advances.

In a special report published Jan. 30 in the inaugural issue of Radiology: Artificial Intelligence, Luciano M. Prevedello, MD, and colleagues recognize the challenges of implementing AI into clinical workflows, but also offer potential solutions—specifically image-based competitions—which could foster faster, more collaborative AI research.

Researchers used in vivo, two-photon imaging to identify a blood-clotting protein responsible for destroying the synapses in the brain—a precursor to cognitive decline, according to a Feb. 5 study published in Neuron.