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Assessing the Value Proposition of AI in Radiology


In the second part of a recent interview, Nina Kottler, M.D., M.S., discussed keys to evaluating the potential value of artificial intelligence (AI) systems and emerging developments with AI that were discussed at the recent Radiological Society of North America (RSNA) conference.

In the current health care climate, return on investment (ROI) may be the most paramount consideration in choosing and implementing an artificial intelligence (AI) system into a radiology practice or hospital.

With declining reimbursements and increasing costs of care, Nina Kottler, M.D., M.S. emphasized that hospitals and radiology practices don’t have extra dollars to invest in technology. In a recent interview, Dr. Kottler noted the combination of increasing imaging volume and radiologist shortages continue to exacerbate the challenges.

“(There are) not enough people coming out of residency to hire our way out of this problem. We need to innovate our way out of this problem,” maintained Dr. Kottler, the associate chief medical officer of Clinical AI and vice-president of Clinical Operations at Radiology Partners.

The continued emergence of literature documenting the diagnostic capabilities and improved efficiencies with AI-powered systems can facilitate improved patient outcomes, allow for more diagnostic exams and treatment interventions to be performed, according to Dr. Kottler. She added that a number of positive developments coming out of the recent Radiological Society of North America (RSNA) conference continue to build the emerging ROI case for AI in radiology.

At the RSNA conference, Dr. Kottler said it was clear that more and more radiologists are recognizing AI “as something they need to do, not just a nice to have.” She emphasized that robust improvements in AI, fueled by engineering-based and cloud-based technology, can facilitate implementation into radiology workflows. Dr. Kottler added that an increased emphasis on AI standards for vendors will allow more of a “plug and play” transition with AI systems as opposed to the current daunting approach of trying to integrate the technology into various aspects of existing systems and workflows at radiology practices and hospital facilities.

(Editor’s note: For related content, see “Recognizing and Addressing Biases with AI and Radiologists,” “Emerging Concepts and Perspectives in Radiology: Video Interviews from RSNA 2022” and “Assessing and Implementing Artificial Intelligence in Radiology.”)

As more radiologists and facilitates incorporate AI systems, Dr. Kottler says the increased experience with implementation will hopefully lead to best practice standards for initial and ongoing evaluations of AI models.

For more insights from Dr. Kottler, watch the video below.

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