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oh
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oh
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oh
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i uh if you had one your print out one hundred after her
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yeah
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oh that's an excellent question so definitely if you have one hundred year of training
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you have seen many cases you have followed up many cases so we have seen images of patients that
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undergoes surgery and then analysis of the tissue then you know all does this image is related to
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just kind of cancer is related to this kind of outcome with this treatment so yes and that's
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why we g. pruning is trained on large data sets it sitting the human ability to learn
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we are not able to learn on ten hundred million images in one year
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oh
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uh
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oh
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yeah my impression is it's dependent of the that that's what
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we call the experience in professor who is sixty five
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your role have seen hundreds if not thousands of not that i know millions of cases he has better experience
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oh
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e.
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so from my perspective my personal perspective i embrace
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this movement since years i'm annotating i'm working
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on the strategy level with the last cooperate startups from i would say uh the
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majority of the radiologist there where kind of if you hear a few years ago and
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uh since i would say one two years the radiologist and the radiology department
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has begun corroborating because they are slowly accepting the idea
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that i i would not replace them at all
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but they will augment them we're shifting from here to a collaboration since two years
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yeah
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i see
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i
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a a a one
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i i i
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uh_huh
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a a a a a a a
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oh yes the from the the answer is straightforward today that
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the answer is no the generalisation over different vendors
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even different plans of acquisition when we make images of
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the brain usually we have to reference planes
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what we call c. a. c. p. or in english uh the op was
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it so it's approximately we this the slides are oriented like these
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i have seen on many application that if you just do a little bit
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the algorithm does not segment the brain re why he does not recognise the structure
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so this is when the same machine when we go over all their machines
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the generalisation face today and this is also
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a problem uh that is widespread in health care what we call inter operability
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there there are no inter operability each vendor has its own structure outside
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it's silent so there is a lot of work to overcome this kind of uh of problem
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obviously if you if you do i'll go over it with images
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from all vendors at some point it could be generous
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but the algorithm does not have a i kissed what i have seen it does not have the knowledge of the anatomy
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yeah and this is very crucial he does not have the knowledge of the treaty structure he's processing
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usually to the images to the slices and is trying to recognise patterns
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that's i believe one of the limitation as a medical doctor we first
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train as doctors knowing the human body we know what the anatomy
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besides medical imaging designs anything we know how brain is is is from the anatomical structure
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that would be one research uh i would say i we are when i was discussing with some research chain to
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try to be inspired by this kind of learning learning for
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the anatomy maybe the actress actresses of the brain
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and then building algorithm that are more aware of this but it
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from what i heard today it's quite hard because we're
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still in to the pattern recognition so the the the answer is no generalisation is not something given to that
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yeah i
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yeah
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i
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uh_huh
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uh
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i i i a
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i
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i
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i'm not aware of uh this kind of inclusive holistic studies but some
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research a unit i can for example talk about i. b. m.
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uh the research team a devoted to medical imaging i had a talk with that one of their team a blaster
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they have this sense of mean making the natural work flow in the organisation and in the mind of
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the radiologist but i'm not aware of a publication maybe exist but i'm not aware of publication looking at these
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no for the beneficial effect of replacing one or two
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steps on the health care system outcomes and costs
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and it would be very hard to obtain this kind of studies
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yeah
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oh yeah at the last good uh_huh
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yeah
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that's the whole point uh having go a wide view of the system
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from starting point two and point and trying to envision and a i uh
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had begun shift how we can transform the system how we can make it more streamlined
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at the whole the problem is health care system are very different from one
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country to the others and within one country it is highly fragmented
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takes with that and uh we have approximately six to eight
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or nine high level hospitals for eight million people
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in the bay area ah around silicon valley have too many hospitals for the
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same amount of people so it gives you a sense of fragmentation
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inside and outside healthcare systems
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any other questions
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oh oh oh oh oh
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i work for a while
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i i i
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yeah
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definitely so definitely we come back to the fragmentation of healthcare inter operability proprietary says
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then silo data uh but uh it is the same challenge in computer vision
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it we have also the same challenge in computer vision we have different
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type of images where different type of machine so those challenges
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are present in all the healthcare use cases what is and
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what i would say this year um is for example
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if you tried to souls the automatic scheduling problem
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you are less dependent of uh of outside and that'll make knowledge
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and you are more dependent on numbers schedules
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roll data that you can more easily process
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oh i
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she
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i wish
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i i think the best
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position people are the clinical that assigns and the uh
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d. global example or it's the n. g. h. and b. w. h.
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o. clinical that assigns so we'd sit at that assigns institute
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which is backed by the hospital they have full access to the to the health care data they have full
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access to the expertise and they have in house doctors
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in house doctor saying just in house product developers
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so they interact daily with the clinicians with the work flow that developing their own prague
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proof of concept then they are integrating does proof of concept into a sandbox
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very localised clinical teen taking feedback and iterating
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dues for me are the best position to bring innovation because they
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are they have the height of expertise of that assigns as
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jointly with the high level expertise of clinicians in a clinical environment
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and this is the key to be close to the clinical environment and then we will see we cannot predict
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how fast it will arrive what would be the impact but my impression is uh
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the administrative work flow would benefit from a i earlier then image analysis
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i i i
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a bear has a open to that uh science institute i was
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invited to be that a similar talk of two months ago
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they are building at that age with anonymous the top and offered the opportunity at least it
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is the project for industry startups research team to log into their that i like
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and develop their own ago if i'm aware of of burned you know with the engine speed that
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but globally uh outing dog but neither is the uh the
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u. n. g. h. can you can that assign centre
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stan ford is also building is it's all know they have it's own a. i. n. e. artificial
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intelligence in medical imaging and they are more uh also oriented into building an open source
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image annotated image trip was it to such as imagine at for radio much

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AI and Radiology
Dr Amine Korchi
18 Feb. 2019 · 11:05 a.m.
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Dr Amine Korchi
18 Feb. 2019 · 11:43 a.m.