From the September 2018 issue of HealthCare Business News magazine


There are additional ways that the information gathered from medical images differ from standard photographic images. Prevedello discussed CT further, where an exam will include a range of numbers. “So 0 is water, 2000 would be bone, -1000 would be air, and -75 for example would be fat . . . different values represent different tissue types, and this is extremely important for lesion characterization. Same thing with MRI, just different process. You have a high signal on T1, it can be blood, it can be fat – the signal characteristic means something. It has a tissue characterization embedded into them. So the way you deal with these images on the pre-processing side is different than a photographic image because you don’t want to lose that type of information, which is key for diagnosis, patient follow-up and the complete understanding of the clinical picture,” Prevedello explained.

The increase in use of AI going forward will undoubtedly impact the work of radiologists, but a lot remains to be seen on exactly how it will impact the work. For now, Prevedello stresses that it’s important for radiologists to at least understand the general concepts of how algorithms are built and how they might fail. That’s why he’s a proponent for educating residents and medical students, because as he pointed out, “you only identify things you know, and if you don’t know about a particular issue an algorithm may have in detecting or classifying something, you may not interpret that information correctly.” Meaning at least for now, artificial intelligence is still no substitute for human intelligence.

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