Harvard Business Review: 3 ways AI marketplaces will change radiology

Andrea Park -

With researchers continually developing algorithms that can interpret imaging data with superhuman accuracy, radiology is the medical field in which artificial intelligence has so far made the most progress.

While most of these algorithms still have yet to be introduced into actual clinical use, several technology companies have recently launched app store-like marketplaces with the aim of streamlining the process of developing, testing, obtaining regulatory approval for and deploying these AI solutions.

A new article from the Harvard Business Review describes three ways in which access to AI marketplaces like those offered by GE Healthcare, Arterys and Nuance Communications will benefit radiologists.

1. Fostering collaboration: Not only do these marketplaces give technology developers direct access to their user base, but they also offer a space for the developers and users to communicate. Radiologists can provide developers with real-world data about actual clinical use of the algorithms, and as a result, developers can produce the most beneficial tools and those best suited for FDA clearance.

2. Reducing burnout: With the development of the algorithms in the marketplace directly informed by radiologist feedback, the AI is all but guaranteed to integrate seamlessly into and optimize clinicians' workflow, rather than adding to their workload. Additionally, the algorithms themselves are specifically designed to take some of the burden off radiologists by instantly identifying abnormalities in imaging data that might take a human expert several minutes or even hours to diagnose.

3. Improving outcomes: Many of the algorithms that would be included in an AI marketplace have been proven in clinical studies to analyze X-rays, CT scans and other imaging data with greater accuracy than expert radiologists. Therefore, having access to a wide variety of AI solutions via a centralized marketplace could lead to significant improvements in patient outcomes.

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