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.
What does Frankenstein have to do with health IT consolidation? Are IT professionals more like Indiana Jones or McGyver? Is there an end in sight to the consolidation trend?
Is AI a threat to physicians? Is it possible for AI algorithms to make physicians obsolete? Will the robots take over? In the second part of this series, Florent Saint-Clair kiboshes some of the many fears associated with AI. And highlights the many advancements the merging of AI and healthcare has benefitted the industry.
Florent Saint-Clair challenges the healthcare community to let go of lingering fear or doubts and accept that in 2021 AI is here to stay.