At RSNA 2017 there was a remarkable uptick in the presence of artificial intelligence. From new companies specializing in it, to established companies embracing it, to virtually everyone at the show talking about it.
HealthCare Business News checked in with Elad Walach, who's company, Aidoc, was one of those newcomers with a special understanding of the potential AI has to disrupt and reinvent the way we think about radiology.
HCB News: It seems like Aidoc really burst on the scene at last year's RSNA meeting - can you tell us about the company's origins?
Elad Walach: RSNA 2017 was an amazing event for AI technology in general, and specifically for Aidoc. It felt to us like the turning point from general interest in the concept of AI, to actual assessments of the various AI solutions out there and how they can provide value to radiologists. We had just celebrated earning our CE Mark to operate in Europe, and it was a great opportunity to showcase our commercial head & c-spine AI solution and demonstrate its ability to streamline the workflow. It was so encouraging seeing all our challenging work pay off and create such traction.
Since founding Aidoc in 2016, we have been on a mission to revolutionize radiology by injecting AI into the radiology workflow to make the radiologists' work easier and more efficient. We're witnessing an extraordinary trend in radiology now. While the number of scans is increasing each year – with an 82% rise in STAT scans - the number of radiologists across the industry is nearly stagnant. In this trend we saw an opportunity to harness deep learning capabilities to address the radiologists' pain points and help their day-to-day. Almost two years on, we are seeing the impact of our technology helping radiologists be at their best, and doing so more efficiently!
HCBNews: RSNA 2017 could be called, "The year of AI." What is Aidoc bringing to radiology that other AI companies are not?
EW:It was incredible to see how many people are trying to advance this technology with such passion and expertise.
We believe that the adoption of an AI solution requires significant and frequent value creation to the radiologists, which means addressing a major share of their workload. To do so, we have developed a novel AI technology that is unique in its comprehensive coverage of the anatomical area. This level of comprehensiveness allows our solution to be applicable in most scans of that anatomical area and detect the various critical findings which might appear in the study. Pinpointing the findings in the image allows our solution to highlight the abnormal cases and optimize the worklist – dramatically reducing turnaround time for the cases which matter most.
