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QA: A New Frontier in AI-Enabled Radiology

Radiologists and healthcare administrators have been waiting a long time for the arrival of promised AI tools for radiology. Most AI applications used by radiology providers today are focused on worklist prioritization. While improving the speed of patient care is incredibly important, recent strides in quality improvement open a new world of possibility.

Q&A: vRad’s CIO on AI beyond worklist prioritization, new solutions for radiologists, COVID-19 and more

Originally published by Michael Walter as Q&A: vRad’s CIO on AI beyond worklist prioritization, new solutions for radiologists, COVID-19 and more on Radiology Business 

As 2020 comes to a close, radiologists find their profession at a major crossroads. AI and other game-changing technologies are rapidly evolving, government policies are forcing practices to rethink their business models, and a once-in-a-lifetime pandemic continues to cause chaos for the entire healthcare industry. 

With that wealth of opportunities and challenges in mind, Radiology Business spoke with Imad B. Nijim, the chief information officer at vRad, about what the future may hold for both his own company and the imaging industry as a whole. Nijim is a veteran of the healthcare technology space, spending considerable time focused on radiology, and has seen the industry undergo countless changes over the years.

Incremental Improvements for Radiologists Can Add Up To Big Gains In Performance

Because I work as a teleradiologist, I (thankfully) no longer have to commute to get to work. Living in southern California, traffic is one of the things I do not miss at all. I used to spend a lot of time behind the wheel driving to the hospital where I worked, thinking of ways I could make my commute faster and more efficient. Admittedly, I’m a bit obsessive when it comes to time and efficiency, especially in the way I work.

What does a teleradiologist experience on the best radiology platform?

 

Working for vRad is pure radiology. Let me show you.

New Radiology AI Models Reduce Time to Care

Accelerating care delivery

I am pleased to share that vRad has deployed two additional Artificial Intelligence (AI) models to our imaging platform, bringing the total to seven active models helping patients right now.

The first new model identifies pneumoperitoneum in chest CTs, and the second model identifies testicular torsion in ultrasound scans. Both conditions are critical, and timely diagnoses will have a positive impact on patient outcomes. As with all our innovation and product development, our models are immediately available to clients as part of our AI-enhanced on-the-ground and in-the-cloud radiology solutions.

I left vRad. This is why I came back.

After 11 years as a vRad radiologist – 3 of them as Clinical Chief of Abdominal Imaging – I left in 2019 for a teleradiology position at another well-known national practice. Just 9 months later, I’m back. Here’s why.

I see AI improving care for stroke victims every day

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.

“In the wild”: MEDNAX and Qure.ai partnership guiding AI advancements

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.)

How we successfully put AI models to work for our radiologists

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.