More Data, Now What? SIIM21 Tech Chat Webinar
The most challenging part of AI in the medical enterprise imaging world is where and how to get the necessary data needed to accurately train the algorithms. The main success of AI, a data driven self-learning algorithm, is based on constant learning by processing ample amounts of data. The more data available, the more accurate the outcomes will be. Even if there is “inconsistent” data, with enough data volume you can simply identify and separate the incorrect data appropriately from the inconsistent data. The key is that more data actually equals less problems, which is the opposite of what The Notorious B.I.G. famously rapped in his 1997 hit song, “Mo Money, Mo Problems.” In the case of data, more is definitely more. More data inherently brings more questions. How will the shared data be collected? Are there any incentives for standardizing the data? Whose data is more meaningful? In this roundtable discussion. Dmitriy Tochilnk, CEO of Dicom Systems and Boris Zavalkovskiy, ENT Director, IT Software Architecture at Envision Health discuss driving AI ready data adoption and the current challenges associated with all things data.