We've been waiting for AI to radically improve radiology, but where's the revolution?
The issue lies with our inability to see the true applicability of AI. The focus has been on finding places where algorithms can be best applied. This narrow view on AI's utility, without true workflow integration, is akin to a hammer in search of a nail.
For example, there are radiologists who repeatedly read aloud numbers on the screen to enter various data points into radiology reports . Used properly, AI would notice that for every CT, the radiologist viewed the image of the dose report and dictated the dose length product from that image into the dose field of the radiology report.
Hanging protocols also offer an opportunity for AI. Automating hanging protocols is a seemingly low-hanging fruit, since radiologists commonly maintain their personal pattern for each type of scan . AI could notice these personal patterns and ensure that each scan is hung according to the radiologist's individual preference, shaving valuable seconds and reducing mental load.Currently, many radiology AI algorithms receive images as input and produce a diagnosis or predicted outcome as output.
Additionally, while a radiologist is examining a body part where surgery has been performed, computer vision could find and display the most recent prior preoperative images of that body part showing the primary mass. This would allow the radiologist to easily compare the imaging characteristics of soft tissue in the postoperative location with those of the primary mass and decide how suspicious to be about recurrence.
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