this is what you've been waiting for
hello world it's Suraj and welcome to my
new course on AI applied to business the
most valuable course I've ever made
we're gonna build a simple AI app for
doctors that lets them upload a picture
of an eye and tells them whether or not
that I has diabetic retinopathy if
you're looking to build a profitable AI
startup or implement AI at your company
in some way this is the course for you
all my videos will be free and available
right here on YouTube so make sure to
subscribe to stay up to date with my
latest content advances in medicine over
the past few decades have improved
health care immensely allowing doctors
to more efficiently diagnose and treat
diseases but doctors are still humans
which means they understandably still
make mistakes on the epic AF TV show
house dr. House's genius
far exceeds his colleagues which implies
that if all doctors were as smart as him
diagnostic mysteries and unnecessary
deaths in hospitals would drastically
decrease but the reality is that the
biggest difference between doctors is
not their level of intelligence
it's how they approach patient problems
and the type of health system that
supports them this combination is what
causes such wide variations in clinical
outcomes and it's the reason why machine
learning is the best solution out there
to improve doctors capabilities if you
can't beat them join them there's so
much potential here for example studies
showed that over half of all women in
the u.s. who get regular mammograms will
receive at least one false positive
which is a test that wrongly indicates
the possibility of cancer in a 10-year
period
radiologists regularly disagree on their
respective interpretations of medical
images AI can do what no radiologist can
it can learn from hundreds of thousands
of medic
images and is estimated to be up to ten
percent more accurate than the average
radiologist that accuracy gap will only
increase as computing power gets cheaper
and can be applied to any of the
countless subfields of medicine not just
radiology like what the startup vizhai
is doing for brain scans doctors also
have to interpret patient medical
records which can be a very complex task
NLP a branch of AI that helps computers
understand and interpret human language
can review thousands of medical records
and output the optimal steps for
evaluating and managing patients with
many illnesses some doctors really are
gifted at what they do like dr. oz just
kidding an AI can learn from the best by
watching them do their work if all
doctors matched the performance of the
top 20% patient deaths from a variety of
diseases would decrease by the hundreds
of thousands per year and wild doctors
have natural biases AI is more likely to
produce objective diagnosis for patients
without preconceived socio-economic
notions which can produce disparities in
care ml will become an essential tool
for doctors like the stethoscope has
been and as more of the profession is
automated human empathy and compassion
for patients will become paramount to
their success in the field also good
looks let's just be real so how do we
pick a problem to solve the AI and
healthcare market is expected to grow at
an incredible forty eight percent
compound annual growth rate in the next
five years according to research and
markets the tech giants like Google and
Microsoft have massive amounts of data
talent and computing power they have a
huge advantage when it comes to building
horizontal products these are products
that can be applied to many use cases
but as a start-up you can build a
vertical product one that tackles a
single problem
very well since they don't have the time
to do that one way to do this is to find
relevant problems in online communities
where doctors congregate
these can be forums slack channels
subreddits Grey's Anatomy chat rooms
about McDreamy and see what kind of
problems they're having another way
would be to call up or schedule a visit
to a local practice to hear firsthand
about the type of problems they're
having listening before acting is an
important first step when finding the
right problem to solve eventually we'll
come across a problem that is dealt with
by multiple doctors let's say for the
case of this demo its diagnosing
diabetic retinopathy correctly we can
build a classifier to help solve this
problem but first we've got to collect
some quality data the foundation of all
machine learning is having lots of
quality data that's how it learns very
few companies actually generates and own
medical grade data since collecting this
data from patients is quite difficult so
we're gonna have to get a little
creative we can search the web by
searching public imaging datasets for
diabetic retinopathy we'll come across a
few some will require us to apply and
register before getting access so we can
go ahead and do that and hope for the
best
alternatively if you're a student at a
university you can go find the nearest
Imaging Research Center and ask the
professor there they're always willing
to help with projects PubMed is also a
great place to find biomedical research
papers we can search for papers by the
type of scan data we're looking for and
once we find a good one we can just
email those researchers directly explain
our project and how it could be mutual
beneficial to both of us
if we frame it more like we're trying to
help them then asking them for something
they'll be more likely to help us if we
can get our hands on a quality data set
ideally with labels because labelled
data sets
are always easier to learn from then we
know that we have a shot at solving this
problem but before we actually invest
our time and energy into building an AI
model we need to make sure that we're
able to get customers the easiest way to
do this is to create a simple landing
page we can find a template pretty
easily online that asks for a simple
email signup we'd explain our product in
detail and the landing page and once we
have it we can send it to potential
clients in our case that would be
doctors and medical companies we can
find a directory of them online and
email them one by one post it in
facebook groups and see if we get any
signups if we're able to get a
sufficient amount of signups then we
know that there is indeed interest in
our product and we can go ahead and
start building there's an entire
ecosystem of libraries and services that
help us build models our kyv sanity is a
great tool to help a search for how the
latest AI models have been applied to
medical diagnosis in research labs once
we find a cool paper we can use it as a
guide to help build our own model it's
important to remember that there are so
many tweaks and modifications we could
potentially make to our model to improve
it but the best thing to do is to first
build a prototype that works as fast as
possible and then iterate from there
convolutional neural networks are a type
of model that have proven to outperform
all others when it comes to image
classification which is our use case the
fastest and easiest way to build a
convolutional neural network is by using
the chaos machine learning library it
lets us build neural networks easily
with its high level API each line of
code corresponds to a different layer in
our neural network we can search github
for an image classification chaos model
and use that as a base model
notice how the parameters all seem like
magic numbers how are we supposed to
know
which numbers are optimal this is the
art of deep learning figuring out the
right parameters for a certain type of
model is the kind of thing that research
papers focus on in this field
sometimes a simple tweak gives rise to a
breakthrough in accuracy we can use
similar parameters to the paper we chose
to start and train this model on our
data set I have a macbook pro love you
Apple let's collab okay thanks bye and
not a deep learning rig so I found that
the best bet for me is to upload my
model to Floyd hub which is a platform
as a service that helps train deep
learning models in the cloud we can do
this in the command line with just a few
commands what it's doing is learning the
mapping between the input data which are
the medical images and the output data
which are the diagnosis of the images
once it's learned this pattern given a
new image it can accurately predict what
the diagnosis is
once it's done training we can use Floyd
Hough to create a basic API call where
we merely send it an image and it will
return the prediction of that image with
any web framework of our choice we can
build a single web page that asks the
user to upload an image it'll call the
API and return the prediction or the
user to see just like that from here we
can make programmatic improvements add a
better design and finally start
collecting some sales hold on hold on
hold on I've got three things for you to
remember from this video hey I can help
doctors better approach patient problems
and improve the health system that
supports them improving diagnosis
interpreting patient medical history and
drug discovery are just a few of the
potential practices that AI can improve
on and we can build a simple model using
the Karos deep learning library then
train and deploy it using floyd hub want
to be friends hit the subscribe button
and I'm all yours for now I've got to
see my robot I mean doctor
so thanks for