﻿Again, we thank all of you for having participated
in this workshop up to this particular time.
Your insights have been very useful.
In this presentation, we actually take a look at
where we are in future trends in the digital space.
In this presentation-- sorry, should I--
In this presentation really, we are focusing on those
initiatives, insights that are current and also both futuristic.
This is, of course, our way of preparing for the future
and harnessing technologies that are yet to come.
In this installment, we focus primarily on two
major topics which is artificial intelligence
and blockchain, beginning with blockchain
and proceeding to artificial intelligence.
We'll commence really by looking at the agenda, the topics.
We'll start off by discussing blockchains,
specifically blockchains application for health.
Then, we'll proceed to look at a number of aspects that have
to do with artificial intelligence and its application.
I'll lead the the blockchain session and my colleague, Honey,
is going to lead on the artificial intelligence component.
Now, before we go very far, I want to stress
the fact that these kinds of presentations
are both strategic and useful for both
ministries of health, ministries of ICT and WHO.
There are common terms in the industry, and so is
critical for us to have an understanding of what
is being talked about and also what are some
emerging implementations in this particular area.
Those of you that have attended international conferences for
example on the African continent, for example, Transform Africa in
Kigali, blockchain was one of the topics that was both discussed
in the main event and also at our side event on digital health.
At the same time on the African continent, for example in South
Africa, there have been a number of seminars on blockchain
itself, and a number of African countries like Uganda, for
example, have developed associations on this particular subject.
What indeed is blockchain?
We'll start off with looking at what's
transpiring globally, specifically in Estonia.
Estonia is one of the first countries or the few
countries that have really embarked upon implementation
of what we're referring to as blockchain that I
will describe in a few slides in greater detail.
In about 2016, the Estonian government,
the e-Health foundation, embarked upon utilization
of blockchain for the medical records systems
that are deploy in that particular country.
As many of you know, Estonia has digitized the economy.
They have about 1.3 million records that are
being carried by smart cards for the population.
What this project embarked upon to do in
2016 was to embrace blockchain as
the means of security and also as the means of
sharing the medical records themselves.
Now, this particular package, of course, is well
described on the e-Estonia website, and if you
have time in the presentation, you can actually
read the whole article on how that was done.
When we say blockchain, what do we actually mean?
This subject was influenced by an article
that was released in about 2008.
An anonymous article that really articulated
the idea of a purely peer to peer
network as a basis for both ensuring
security and transparency in transactions.
This really was looking at electronic currencies,
cryptocurrency, otherwise referred to.
What was really advocated in this particular paper which
is indeed the utilization of peer to peer networks.
That is to say machines that have direct connections with each other,
that notion of removing
an intermediary in between like a broker.
The idea of having machines communicate with each
other was the basis or this particular article.
Blockchain as a concept rode upon this theory.
When you really actually say blockchain,
indeed there are no physical blocks or chains
but is an idea of information packages that
are linked together which form a chain.
In this particular case, this diagram is quite useful.
It summarizes the high level implementation of
what the blockchain would actually look like.
One first component is that the items of information that are
distributed across a network are referenced to each other.
The block of information that is in front points to its predecessor.
What that basically would mean is that if you package
information in a block or a location, that information is held
securely, and once you make an edit or an addition,
the subsequent record has a reference to the previous records.
All the machines in the network have full
view of that particular transaction.
The foundation of blockchain is really based
upon being able to link information packages
called blocks and the chain, which refers to
the referencing of the subsequent blocks.
Within the blockchain or the block code area of your information,
you can actually store the actual data, which can be anything.
It could be medical record data, it could be information
about your title deeds, it could be agriculture information.
The actual content in the block can be anything.
In this particular case, we're trying to see how we can
actually utilize that capability to store medical records.
Then secondly, around the block is reference to the subsequent block.
In some implementations, there is additional information
or metadata called a smart contract, which is really
a piece of information, or metadata that informs how
the block should be either accessed or how it should behave.
So what we're really talking about here is
a kind of a distributed database that has
full view of all the machines that are
participating in the particular network.
Moving forward, what blockchain terminology includes
is the notion of what are referred to as miners,
who are really either machines that are viewing
the blocks in the network or individual end users.
These particular miners, or participants,
who may be machines or physicians, have
the responsibility of viewing and ensuring
that the block network is not corrupt.
That is the power of blockchain, being able
to ensure that there is no corruption,
data is stored securely, and also, this
arrangement makes the network of blocks.
There's an English word called they are immutable.
That means to say you can't alter the record
because any change, all the machines,
or the miners, or the participants, must agree that
this alteration is indeed a valid alteration.
So the principles in this particular case of blockchain as follows: 1.
There is indeed must be a network of computers, and that's
one reason this conversation is indeed taking place amongst
ICT people because they control the infrastructure
and the health people because they control the medical information.
So there indeed must be the environment upon which
this network or infrastructure should ride upon.
Next is the availability of the protocol,
which is, in this case, the software
or infrastructure that ensures that the blockchain itself is managed.
For example, Ethereum is one of
the protocols that are used to ride on
the network to make sure that the blockchain
transactions do indeed take place.
Also, lastly, the ability to make consensus or agreement.
When you make a change, is this change indeed a valid change?
The third layer is the ability of a mechanism for machines to agree
to the particular modification that is taking place on a record.
Some of the principles include transparency.
That is to say that all alterations
or modifications, additions, are taking
place in a transparent manner,
authentication, that means to say that
the information requires security protocols
for you to be able to access it
or view it, and also auditing, because
you have a threat, which is a change.
You can actually go back and see what the record was before.
There are a few examples of
the implementation of blockchain in health, in
this case, the advantages of blockchain
in health, key is data security.
We speak of security so much, especially
when it comes to medical records.
The record must be secure.
If you recall, many years ago, when we started implementing medical
records, the key question was, you're moving the data from a register.
How secure is it going to be once you automate it?
Indeed, even recently, we have seen or read
of computer breaches, such as what we had
early this year, whereby a number of hospital
machines were actually affected by virus.
The question of security is key, so blockchain provides that level of
security that assures confidence in the integrity of medical data.
Patients safety is the second benefit.
As I mentioned, on the block, you can add
specific code called a smart contract that
can either contain information such as
alerting the viewer of the block when you are
adding that this particular record, for
example, this patient, cannot take such a drug
or the drug you're about to add is contraindicated
with the drug that is on the block.
Now, remember, the block itself can either contain the actual
information or a hashed reference to information stored externally.
So patient safety is another advantage.
Then the third advantage that we're describing here is
security, which is really linked to the first component.
Then thirdly, within blockchain, we can now, for the first
time, trace a particular transaction from end to end.
What we're really talking about here is traceability logistics.
If you produce a product here, you can
trace it until the point of a delivery.
That means that financial
accountability can actually be enforced
because you have full view of the record from first to last.
Because blockchain also sits upon the idea of
electronic currency, we hope in the future
we can actually use or enable electronic
payments for seeking healthcare services.
Lastly, there are some devices that do intelligent things.
This is referred to as the Internet of Things.
The idea of devices to communicate with each other.
We're seeing a little bit of this with Fitbits,
those rubber bands, those devices that we
put on our wrists, which, of course, have
the ability to speak to your phone or a provider.
Now Internet of Things benefits from blockchain
because the key question around devices
speaking to each other is, is this device allowed
to make that modification or to submit data?
With blockchain, that device can actually write on
the blockchain environment and the blockchain can
serve as a way to authenticate, indeed, this device
is allowed to make such a modification or change.
Now again back to the actual new world in terms of implementation
of the blockchain, I've already talked about Estonia.
They have moved their medical record into the blockchain
environment for the reasons we've talked about.
Implementation in Estonia involves implementation
of medical records on the blockchain, of course.
Also, that allows for things like
e-prescriptions and e-ambulance services.
Also in Switzerland is also a move towards
implementing blockchain based on smart contracts.
These examples are one of many examples that exist.
Now in my last slide, I'll simply talk
about some of the software tools that
are available that one can use to
actually implement on a blockchain setup.
First is a tool of software or technology called Medicalchain,
which, of course, emphasizes its implementation on transparency.
Now what this basically means is that both the patient and the provider
must be able to see the records and agree that the record is correct.
That's one implementation.
Now MedRec is another implementation that is worth noting.
It runs on the Ethereum open source infrastructure.
What MedRec actually does is it extends the idea
of-- if you recall the first day, we talked about
patient health records, which are electronic
records that the patient manages him or herself.
What this particular implementation does is it empowers
the patient to both provide access to a physicians.
I say this record can be viewed by that physician, otherwise, I
can also say this record mustn't be viewed by that physician.
It gives power to the patient to be able to make those kinds of decisions
based upon the ability to view
the actual record that's on the blockchain.
There's NanoVision which combines the power of
artificial intelligence and also molecular level data.
This is another growing interesting implementation because
it combines AI plus the idea of medical record systems.
Now Gem is also another interesting piece of software again like MedRec.
Gem was actually a-- it was
a subsequent implementation from MedRec.
It extends MedRec and enforces the idea of giving
the patient control over their own medical record system.
Then lastly but by all means not the least is SimplyVital Health.
Again, this platform is based on blockchain
technology, but the main motivation for this
particular implementation is patient rights
and also visibility of the patient record.
The notion of blockchain as I mention at
the beginning is a step towards making sure
that you have a secure environment when
it comes to medical records management.
It also gives us power to overcome the issue
of interoperability because the record or data
in the blockchain is available to all
the machines that are within that particular network.
That overcomes the issue of availability because all
the nodes on the network can see the actual record.
Then lastly what I would like to emphasize is
the collaboration between ICTs and health.
As I mentioned at the beginning, this technology is heavily ICT dependent.
This is why the ministries of ICT comes in to
support ministries of health in the implementation.
The role of the ministry of health, of
course, is to ensure there is automation in
terms of digitization of health such as
implementation of medical record systems.
With that, I'd like to suggest that we
move forward to the next presentation on
artificial intelligence and then we'll take
the questions at the end of the next session.
Merci beaucoup and obrigado.
Thank you very much.
[applause]
Speaker 2: Okay.
Actually, the reason why we wanted to close this whole training
by talking about the future is not only for you to be aware
what will come but also the importance of what we need to do
today in order for us to leverage what will happen tomorrow.
As you all see, AI is definitely one of the technologies that would
have significant implication on how healthcare will be delivered, et
cetera, but if countries want to leverage the power of artificial
intelligence, basically the work that needs to be done today is key.
Otherwise, if you don't have the right foundation and the right
data, you will not be able to leverage those technologies.
It's extremely important.
If you don't want to be sidelined in the future, you
need to prepare from today and set the foundation.
Let me maybe talk a bit about artificial intelligence.
Artificial intelligence by no mean is a new concept.
It is not a new concept.
People started to talk about the ability for machines
to think as human since long, long time ago.
However, for the last few years, there has
been some advances in technologies in
some advanced mathematical models, et
cetera, that meet this potential reality.
This is definitely a very wide topic, it could not
by any mean be covered in half an hour, but I'll
just give you a few sense of what does it mean
and what are the potential and challenges, et cetera.
I would like maybe to highlight few terms that are very,
very common in the area of artificial intelligence.
First of all is machine learning.
Usually, we are very much used to be able to program a computer to say
this is what the machine will do, one, two, three, four, five, six.
The machine learning is very different from that in
the fact that machine can start to understand and analyze
and create their own models based on how they understand
the linkages between some set of input and some set of output.
The whole idea is that machines can detect models
and understand the relationship between a number of
input data and a number of output data through
the application of some mathematical models, et cetera.
The machines doesn't need to know anything his
particular knowledge about the domain, is just advanced
statistical mathematical model where you can
understand is the relation between input and output.
I'm going to give you much more examples
about what does this mean exactly.
Deep learning is another term that is very much used.
Let's say it's almost like an advanced version of the machine
learning using this new way of computing based on neural networks.
There are new paradigm in the computing that is using how
our brains are thinking, it's called neural networks.
By applying those computing methodologies, you've been
able to treat a huge amount of data, huge amount of data.
A machine deep learning is exactly the same as machine
learning but that can run on much bigger sets of data.
Again, the whole idea is to try to understand if we have those
input and if we have those output, what is the relation?
Is there a relation?
Because if we understand that there is
a relationship, then we can predict.
The machine learning and deep learning are tools that are used not
only for health, well, they are used for anything, they are very
good in detecting and protecting and understanding relationship
between data that usually and previously would not at all detected.
I'm going, again, to give you very concrete
examples about how this applies to health.
Another concept that it's important to know, because those
terms you're going to hear about them a lot in the future.
Natural language processing, NMP.
Natural language processing is basically
for a machine to understand how we speak.
The machines are very used to very structured
data, but when we speak, it's a natural language.
However, machines now they have much
more power to understand what we mean,
so they just can be understanding
the words but the meaning behind the words.
This creates open up a whole set of new possibilities.
Again, I'm going to give you some
examples of how this applies for health.
Augmented reality where you can mix the reality like videos or real
images, et cetera, with digital
information, and how we can link those
two environments completely where you can, for example, do anatomy
without having to go into or to do incisions and things like that.
It can take the reality but augmented with
additional layer of digital information
and link those two environment that are normally
very separated but they can link together.
Chatbots, another term that you will hear probably a lot about it,
if you are not already doing it, it's a merge about two terms.
Chat which we know all those chat tools, and bot
is coming from robot, so it's a robot chat.
Meaning it's a way, it's a tool, or it's
a technology that can create some sort of
interaction, a machine that can respond to you
in a way just by talking to the machine while
you type or you talk, so you can type something
in a chat and then automatically the machine
will respond to you because the machine were
able to understand what you are saying.
You can say anything.
What is the temperature today?
The machine will understand that you are inquiring about the temperature.
Of course, we have all this functionality now
in our phones in this what you call a virtual
assistant, when you speak with the phone, like Siri,
for example, of iPhone and the same for Android.
It has, again, a lot of very high potential
application in the health domain.
Provides learning is another way.
The way we train the machine, there are
different ways to train the machine.
One of the way is called supervised learning,
where we provide the training data sets.
We say you can build up your model by giving you the training set.
For example, we have lists of patients with those
conditions, and then we can say those patients, this
one had this condition, this one doesn't have this
condition, et cetera, et cetera, and the machine can
use this dataset to train and identify the model that
links between once the motor is identified than
the machine, in the next iteration will be able to detect
and identify the conditions from any input data.
Those are terms that apply for efficient intelligence in general.
Now, how this applies for the area of of health and how
this can be indeed a potential game-changer for health?
In that sense, I too had put a primer paper, it's called AI for health
primer, which is like two, three pages
that we give you in like-- if you
read it in 10, 15 minutes, you will have a primer what is and you will
understand what are the main terms, and what does it mean, et cetera.
This primer, we have put it on your USB that you
would receive at the end of your workshop which you
can read later on to get a very basic sense about
what is AI for health and what is the potential.
Basically, what we were trying to explain that,
as I mentioned, is AI basically is this number of
technologies that come together to create this ability
for the machine to understand and think like humans.
I probably mentioned many of those.
What I want to highlight here basically
that in the way those machines can run,
they can approach to large extent, if not
even surpassing, the human accuracy.
In many cases, the accuracy that are coming out
of those AI algorithms are extremely accurate.
Just to give you a quick example of how
those AI can extremely be powerful, here
is recent technology that has been developed
by a company, it's called AlphaZero.
The company is DeepMind, but the technology
that was the solution is AlphaZero.
Basically, this algorithm is able to train itself, and they
apply it for the check chess player, how to play check.
The algorithm were able to, in four hours only or less than
hours, learn itself how to play check and beated the most
advanced computer program that play check that has been based
on millions or huge amount of data over a number of years.
In four hours only, the algorithm were able
to train itself and beat this other program.
It just give you the amount and the power that this technology can do.
Deep learning is being used particularly, for
example, in the ability to analyze medical
imaging, for example, whether this is like brain
imaging images, X-Rays, et cetera, et cetera.
Again, this can be used in a very powerful manner in
order to, for example, detect diseases even at early
stages, which can be used for prevention, for example,
and to avoid any future or potential complications.
The natural language processing has been used,
for example, to be able to understand
the natural language and to mine the information
that are in the HR records that are written
by doctors where they are writing their
commands, but now the machine can go and try
to understand what is the meaning of those
comments and try to create some insights.
Medical chatbots where you can chat with this chatbot.
You can write your concern, write your question,
and then the machine can respond to you.
Whether it can be either useful to get information related
to some health area, but it also has been used, for
example, during emergencies, where you need very quick ways
to get information because you would have a lot of demand
and you don't necessarily have enough operator, so you
have those medical health chatbots that can respond to key
issues during emergencies that are related to emergencies
or some health-related issues, first aid, et cetera.
As you can see, everything is depending on having data.
The business of the future and the real
value of the future is having those data.
Actually, the real value will come not from
those who have the algorithm, not at all.
Actually, it will come from those who have the data because if you have
the data, you can, using people who are
trained on data science, on artificial
intelligence, they can get huge value out of the data, but if you don't
have the data, then you will not be able to leverage the power of AI.
Basically, the organizations who will have
the data and have huge amount of data that are
also of high quality and well integrated
and the interoperability, et cetera, those who
will be the one who will be able to leverage
these, create algorithms and create insights
and create value, that would be leveraged for
the improvement of the health population.
Those countries or those organizations
who don't have will be sidelined
or at least they have to pay a lot of money to get this power.
Really think about it with the advent of
AI, the real value for the future is data.
All what we've been trying to do in
the workshop is really trying to put the right
foundations to build up this core essence
that will be extremely useful for the future.
Quick points, thinking about the potential of AI for health.
If you think about it really, the AI can be a game changer
for universal health coverage, how this can be done.
Actually, now you will be able to do
diagnostics and early detection and improving
treatment through those AI types of technologies
at the point of care by using maybe
a very simple mobile phone because, for
example, AI is very powerful in image
recognition and image understanding and image
analysis, image recognition particularly.
Basically, if you have the right
applications with the right algorithm, you
can detect a number of issues, and we're going to give a few examples.
For example, related to skin cancer, for example,
you can simply detect it with a mobile phone.
About detecting early cases of retinopathy.
Just with a simple mobile phone, you can take a picture with mobile
camera and then the AI module can
really provide you with analysis of
the case and whether there is an early
case of retinopathy where you can
treat it in advance and can prevent indeed the areas of complications.
Currently, in telemedicine what happens is
that we capture the image and then send to
an expert and then the experts come back with his
own analysis and recommendations, et cetera.
With the AI, basically, this can be done on
the spot without even needing to go back
to-- you can do at least a first triage
or a first detection using those simple tools.
You can also really move many of the power of
detection to the hand of the people themselves.
So you will have a lot of cases where you can detect a lot of
conditions without necessarily having to go to the hospital.
AI, of course, doesn't come with no challenges
and new emerging issues that needs to be dealt with.
Probably, as you can imagine, there's a lot of ethical
issues that will be, that are merging with use of AI.
Because now, for example, by analyzing and using
AI algorithms over your health data, maybe
you can detect that this patient is likely to
develop some chronic conditions in the future.
If health insurance are capable of getting access to
this information, this might create some sort of ethical
issues but also inequality issues and issues that are
against the privacy and confidentiality of information.
Also, many of those AI algorithms are very much as a black box.
You don't know what is going inside,
which means that for many cases, you are
not very much sure of the quality of
the AI and to what level you can really
dependant about the results of the algorithms,
and you don't necessarily have
the high confidence about the trustworthy
of those medical algorithms, et cetera.
This creates a number of new emerging issues about how we can benchmark
and how we can assess and validate those types of algorithms.
Of course, all the regulatory and policy environment
is not yet ready to adopt those types of technologies.
Again, the issue of having the data is key, which
goes back to many of the topics that we've
been trying to cover in the workshop about
interoperability and integration, et cetera.
It means, if your data is still in very much silos, et cetera, as
I said, this will prevent you to leverage the potential of AI.
As mentioned, this can be also creating some inequalities.
For example, if your dataset your supervisor
data training is for a particular
group of people, for example, for only white people who lives in North
America, which means that those algorithms
for those who have those algorithms,
and those who have the data will benefit more from the insights that
are coming from those algorithm, but for
those groups where there is no data
about them enough to create the models
that are relevant will not be able
to be in a position to leverage those new opportunities, which means that
AI might create indeed, in many cases or some cases, some inequalities.
Then the biggest question actually is ability
to have high-quality data that where you
can use for also learning of the machines
and really can create those types of models.
I'm going to go through this relatively quickly but just to
give you a sense of how AI applies for a number of areas.
Let's take primary care and service delivery in particular.
AI now is already used, for example, in
the diagnosis and early detection of
chronic diseases, particularly in the area
of cardiovascular diabetes, et cetera.
The idea is that if we have the right information about and the right
electronic health record about the patients, then you can detect
early signs of diabetes complication or you can define, for example,
people that who are at risk of developing those chronic conditions.
Which means that you can, in very much in
advance, be able to alert and raise awareness
of soft spacious for avoiding developing
those chronic conditions in the future.
Very interestingly, some of the things that
are being very difficult to detect, things
like, for example, autism were very difficult
to detect, particularly for early childhood.
There are now trials and algorithms who by just analyzing through AI
algorithms videos taking for children,
they try to detect signs of autism.
Autism is sometimes very much detected
very late in the process, which
would be more difficult to address, but just by observing children by
taking a video of the children even in a classroom, you can already
detect by AI early signs or signs of autism that are not yet detected.
For example, the children who have some
difficulty of hearing impairments et cetera.
Cervical cancer is another very concrete case where very simple image
recognition conditions can, again, detect some early signs of cancer
just simply by taking a picture and run an AI module on the picture
to detect early signs of precancerous
lesions from the cervical.
You can now analyze the pictures of the blood to detect
tuberculosis and malaria with some equipment that can be used in
remote areas where you can immediately detect by analyzing
the images of the blood to early detect malaria and tuberculosis.
Even snakebite detection by just taking a picture
of the snake, the AI will be able to recognize
which type of snake is this one and what are, of
course, how you can treat actually the snake bite.
The examples goes on and on and on, but let me maybe go to another area
of applications, for example, for emergency response and outbreaks.
One of the possibilities, for example, that you'll see
a lot of use of AI is, for example, using satellite
imageries or even images taken by drones that in case
of disaster where you can simply analyze a picture
of wide areas of affected areas and by applying AI
algorithm, you can immediately detect the areas
and level of damage, and you can immediately identify
the priorities and level of effort required et cetera.
It's already used significantly, so detection of satellite
images particularly on how you mitigate risks in some areas.
I think it was mentioned also in the previous
presentation about using of social media to detect of
the of outbreaks, because now the AI can understand
natural language, you can mine social media.
You can detect alerts or you can detect early
signs of outbreaks which can be rumors or can be
a reality, but just analyzing and mining
the data from the social media, you can have very,
very, very quick signs even before countries
themselves are able to detect this, you can already
detect those are the signs just by analyzing
how people are writing of the social media.
There are some implementation in in emergency
where just by analyzing of tones of the people
that's talking and understanding they are concerned,
that can be dispatched to the right person.
In some cases, maybe the machine itself
can respond automatically to your
question before being dispatched if the machine are able to respond,
which will help significantly in how you provide this quick emergency
support for people who are affected during emergencies, et cetera.
Again, we can continue to talk about that for a lot of cases.
Let me just show you just a couple of concrete examples.
For example, this is a case of retinopathy.
Of course, this is a big problem.
As you know millions of people have diabetes and the diabetic
retinopathy is probably one of the main cause of blindness worldwide.
Of course, as we know, there is a shortage of specialists
who can detect those types of conditions early on.
How we address this by AI for example, here is how it works.
Simply you take a picture by your phone or even a low-cost camera
and you upload this picture to a cloud where the AI algorithm sits.
The AI algorithm will be able to make
an assessment because the algorithm has
been trained from so many pictures who
had, who didn't have this condition.
The machine now can make the link between the input, which
is the image, and the output, which is the diagnosis.
If you say, "Here is a million image about the images of the retina
from people who has or didn't have this condition."
The machine can understand, "Ah, if I see this picture again, I can already
detect whether this person has or doesn't have this condition."
The algorithm that sits on this cloud can immediately provide
diagnosis which goes back to the health worker, but at the same time,
you can use the diagnosis to go back to an ophthalmologist
who confirm or don't confirm the correctness of the diagnosis.
Basically, you are using those cases to feed back into
your algorithm to improve the accuracy of the algorithm.
The more you can bring more data that are validated,
the more the algorithm will be more accurate.
While you are already providing some
value, you are using this data to improve.
Over time, the algorithm can be very, very, very, very accurate.
This has been already piloted in India with level
of accuracy that is much higher than specialist.
Imagine that simply with a mobile phone,
this application, by the way, exist already.
We are already in the AI era, is not something that will happen.
I told you about this example from the snake bite.
It's basically about building a database of all the types of snakes
that exist worldwide and being able to detect that immediately.
I told you also about the skin cancer or any
skin problem where immediately by just taking
the picture, it's very similar to
the retinopathy but applied to the skin diseases.
For example, this application is able to
diagnose 250 different types of skin diseases.
It doesn't mean that we are there yet.
I'll give you an example where we are not there yet.
For example, this application, it's an early innovation to
detect the malnutrition infants or in children; newly born.
As you know, this is a big a problem.
Sometimes it's a bit difficult to detect
malnutrition because you need to do some
measurement and you need to be a bit
trained to do those types of measurements.
How the applications actually solved this issue?
Actually, here is how it works.
Basically, someone not trained at all being
able to take a picture of the kid or the child.
Then the AI algorithm can able to detect what we are looking.
This was also at the arms and the legs.
From the movement, the machine can understand whether
this is the shoulder, this is the arm, et cetera.
By doing that, they can immediately being able to understand
the size and weight and all the characteristic of the body.
By comparing this to other datasets, they can provide some
indication whether this is a case of malnutrition or not.
However, this application didn't yet reach the level of
accuracy and the operation that can make it a game changer yet.
For example, it's very difficult to get
the child sit and be taking the picture.
It takes more time than the manual measurements to
take the right pictures that algorithm can use.
Also, the algorithm is not yet been
able to give you feedback if this
picture is of enough quality to detect
the malnutrition case et cetera.
There're still a lot of research to be done, a lot of improvements.
I just wanted to show you that if this
application is simply by taking a picture
from a child or a newly born, you can immediately detect the malnutrition.
Anyway, this is just a 30 minutes teaser about the AI.
It is by far the most potential trend that will have
probably a huge implication on health care in the future.
In that sense and if you want to know
more about the use of AI for health ITU.
We have created a new what we call a focus group for AI for health.
The AI has a website.
I didn't put the link, but it's very easy.
You just type ITU Focus Group AI for Health and you will get it.
The AI is open for anyone who wants to participate whether
to share new algorithms et cetera or just for learning.
We have physical meetings but also we have a lot of
content and resources that are available online.
One of the main things about this focus group that
it will try to provide frameworks to validate
the algorithms to create some benchmarking that
you can say this type of algorithm we have
benchmarked and it performs at this level of accuracy,
so authorities and ministry of health would
have the confidence of being able to use such
algorithms using those types of benchmarking things.
Anyway, I'll stop here.
The core message is to say that the future will be
really dependent on your ability to have data that are
integrated, not siloed, and accumulated over a long
period of time where you will be able to get to deduct.
I mean, we've seen in the morning that
AI would take that analytics to much,
much further possibilities and we can really improve, but if we don't
act today to prepare those foundations,
then we will not be able to leverage
at least fully because some of the algorithms are not data intensive.
Some of the algorithms, they don't need to have
huge datasets et cetera but some of algorithms are.
Anyway, I hope that this gives us appetite to learn more about the AI
and also to be able to start to build capacities about data science.
Data science is the science of the future and we've been
talking since the beginning what are the different skills.
Here is another set of skills that would be required to leverage those
types of trends in the future around data science, which means that
universities, academia, ministries of health need to start to build their
own capacity and understanding how they leverage this new science.
Thank you very much for your--
[applause]