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 vRad Radiology AI.

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

At vRad, AI is helping manage regulatory compliance and adherence to best practices to ensure quality care, which should avoid unnecessary medical expenses and mitigate malpractice risk.

Our AI models are at work today prioritizing critical cases, improving reporting accuracy and assisting billing compliance. But we’re just getting started. AI will permeate the practice and business of medical imaging to empower radiologists, conquer mountains of administrative burden and strengthen health care delivery.

More than 50,000 radiologists and industry professionals recently gathered at McCormick Place in Chicago for RSNA 2019. Among them was Shannon Werb, President and COO of vRad, a MEDNAX Radiology Solutions company. We caught up with him to get his take on the show.

The vRad Imaging Platform employs multiple AI models in processing over 20,000 radiology studies each day. Silently prioritizing critical cases is one way our radiological AI is improving patient care.

Beyond theory, vRad is using practical AI to impact patient care today, while paving the way to the future of radiology.