No one anticipated the rapid onset of the COVID-19 pandemic, nor the plunge in imaging demand that followed. The question on everyone’s mind is, “When will we return to ‘normal’?” Despite predictions that a drop of 50% to 70% could last 3-4 months, early indicators show volume returning in many parts of the country already.

vRad AI models are at work today prioritizing critical cases like strokes, improving reporting speed and accuracy, and assisting billing compliance. Below, neuroradiologist Josh Morais, MD recalls how AI helped promptly identify a stroke victim in need of immediate treatment, and offers his perspective on the daily impact he sees from AI.

Skilled radiologists can be selective about where they practice. Following are the top reasons over 500 across the U.S. have chosen a job with vRad.

Every four minutes someone dies of stroke; it ranks in the top five leading causes of death and disability in the United States. Rapid response can mean the difference between life and death, as well as whether a patient will live with disability due to stroke complications.  

The video below includes images from the actual CT study of a COVID-19 patient in the United States. As this disease spreads rapidly worldwide, chest CTs are emerging as a critical diagnostic tool for this infection. The likelihood is high that more radiologists will be called upon for similar studies. Please share this case with your colleagues.

MEDNAX Radiology Solutions’ massive, diverse dataset is an incomparable real-world testing ground for validating radiological AI models. Following is an excerpt of Brian Baker’s comments from a recent interview in The Imaging Wire. (For the complete article, which also includes Imad Nijim of MEDNAX Radiology Solutions, and Chiranjiv Singh of Qure.ai, see The Imaging Wire Q&A: Qure.ai and MEDNAX Validate AI in the Wild.)

3 to 4 billion people lack access to medical imaging and its potentially life-saving diagnostic insights, according to the World Health Organization (WHO). RAD-AID was founded in 2008 to answer this need. What began with a few radiology professionals has grown to include more than 10,000 volunteers from 100 countries, 75 university-based chapters, on-site programs in over 30 countries, and an annual conference on global health radiology.

Two components are essential to successfully establish, develop and sustain applied AI in radiology: Uncompromised collaboration between radiologists and computer scientists, and access to a constant flow of data that is representative of a highly diverse population. This is the foundation of MEDNAX Radiology AI.

Many radiology practices talk about visionary AI, but there are only a few implementing AI into their workflows.