The 10 Most Innovative Companies Bringing AI to Healthcare The 10 Most Innovative Companies Bringing AI to He | Page 51

AI
T
he AI transformation is remarkable for its speed. For example in conference such
as RSNA (Radiology Society in North America) and ISMRM (International
Society of Magnetic Resonance in Medicine), AI applications was still a niche
area before 2016, but is the top-1 hottest topic right now. We have passed beyond the
point that people doubted the applicability and potentials of AI in radiology. Machine
Learning and Deep Learning algorithms have demonstrated good performance that can
supplement and verify the imaging tasks for radiologists. Although still in early stage,
variable AI algorithms have already started to be test in clinical environment. This year
2018 is also a landmark year for AI in radiology, since several AI+radiology products
have received FDA’s nodding that they can practice in clinics for segmenting heart from
MRI, make classification from CT, evaluate lung nodules, etc.
Beyond Classification - AI Improves Entire Radiology Workflow
Particularly, when talking about AI for radiology, most people think of an AI algorithm
that can eventually “replace” radiologists and conduct diagnosis. When people talk about
the possibility of “replacing” radiologist, it is important to recognize the complexity of
radiology. Radiologists are imaging experts with more than 10+ years of medical training,
who render diagnosis and clinical decision suggestion from interpreting medical images
and correlating their findings from images with other exams and tests. Currently, most AI
algorithms are often designed for simplified ImageNet-ish image classification tasks, to
separate images from different categories such as with or without abnormality. It is clear
that there are still huge gaps between predicting a classification label, which is well
studied and achieved by AI algorithms, and rendering final diagnosis, which is much more
complicated and requires both images and non-image information.
Beyond image classification, however, AI actually has great potentials to improve entire
imaging/radiology workflow. AI algorithms and products can supplement existing
radiology solutions, with more efficient and accurate imaging acquisition, reconstruction,
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