It is important to note that each model’s individual performance, as measured by sensitivity and specificity, will determine the viability of its use in a production environment. Each model must be refined to meet acceptable thresholds for false positives and false negatives before it can be employed effectively in any workflow.
We’ve just begun to scratch the surface of all that is possible. A single triage workflow may employ tens or even hundreds of models. However, there are less complex models showing great promise for driving operational improvements. For example, one AI model in use determines how many images are in a chest x-ray, even when there are multiple images on the same pixel matrix. Another model determines if the chest x-ray is lateral or frontal (AP/PA). Both of these relatively simple models have immediate practical benefits as a surprisingly large percentage of chest x-ray studies are ordered with incomplete or inaccurate metadata. This quick fix of the metadata using AI avoids potential billing errors and will ensure the images are displayed properly without interrupting the radiologist.
What the future holds
As AI proves its value to radiologists, radiologists are embracing and facilitating the evolution of AI.
The future of radiology is rooted in a collaborative partnership between imaging practitioners and systems engineers. Ultimately, the goal is to improve overall quality, which also improves efficiency. Success requires AI integration into the practical workflow. Automating mundane tasks that are better handled by a computer should eliminate distractions and reduce error rates. Meanwhile, each diagnostic report, through the use of NLP provides essential data to fuel the continued improvement in AI for radiology.
Achieving the full potential of practical AI in radiology requires a strong analytics foundation and deep workflow integration. It requires continuous collaboration between technical leadership and clinical leadership. On a daily basis, they must evaluate AI use cases and ask one another: What problems can we solve? What task can be automated? What are the results? How might we improve patient care?
It is exciting to work in a field that embraces new technologies and AI is poised to be transformational on multiple levels. As we build and deploy AI models, early successes are generating excitement and hope for continued innovation that will enable radiologists to enhance standards of care.
About the author: Imad B. Nijim is the Chief Information Officer for MEDNAX Radiology Solutions and Virtual Radiologic (vRad), a MEDNAX company. He is focused on driving continued innovation at the intersection of radiology and the technology that supports it.
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