Beyond the hype: How practical AI is enhancing radiology

Beyond the hype: How practical AI is enhancing radiology

March 15, 2019
Artificial Intelligence
Imad B. Nijim
By Imad B. Nijim

Moving past the initial media hype, practitioners are beginning to demonstrate how AI applications can enhance the ability of radiologists to support better patient outcomes.

It’s a fact of life in our digital age that emerging technologies are often accompanied by overinflated expectations about their potential to transform the world.


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Consider drones. Early supporters predicted that drones would soon shower our neighborhoods with smiling Amazon boxes and hover at our doors with hot Domino’s pizzas. Of course, they’ve since recognized many barriers to the drone distribution model. Setting aside original forecasts, engineers are finding many uses for drone technology in everyday applications.

With the exciting prospect of artificial intelligence in diagnostic imaging, prognosticators foretold of an overnight medical revolution – some even predicted that computers would eventually replace the need for physicians altogether. Thankfully, we are moving beyond the initial hype. For example, healthcare leaders are asking practical questions to leverage AI to improve patient care through better workflow, higher quality reporting and optimized efficiency. As a result, we seamlessly weave practical AI algorithms into daily workflows with the goal of enabling physicians to deliver the best care possible.

Realizing the potential of AI
Diagnostic imaging is an area of great potential for practical AI.

Image-based and natural language processing (NLP) models are being implemented today into clinical workflows and have demonstrated early success. For example, smart worklists are using AI to prioritize studies to ensure the right study is presented to the right radiologist. Other AI models are built to relieve radiologists of relatively mundane tasks, so they can focus on delivering high quality interpretations. In short, AI enables radiologists to focus their expertise where it matters most.

As an example, AI is proving to be a valuable triage tool. In an optimized radiology environment, studies that are part of a trauma or stroke protocol are prioritized in the physician’s worklist. Studies that are not part of a trauma or stroke protocol, sometimes contain a condition that requires expedited attention. A facility may request an interpretation of images “non-emergently,” but the patient may be experiencing an urgent condition – such as intra-cranial hemorrhage, pulmonary embolism or aortic dissection. AI can look at the images and identify the study for proper prioritization in the worklist.

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