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Revolutionizing Healthcare: The Future of MedTech with AI and Advanced Technologies
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00:00Bonjour à tous, je suis Christo de Hong Kong et c'est une très belle expérience ici à Paris, ViverTech.
00:08Et aujourd'hui, je veux parler de la médicale training.
00:12L'agent population et la shortage de médicales professionnels
00:16est devenu une plus importante issue dans le monde.
00:20Et nous voulons à l'adresser à cette issue de mieux médicale training.
00:24Et comment ?
00:28C'est ce que nous avons créé.
00:30Nous avons créé l'adresse 22 ans.
00:33Nous sommes maintenant en partenariat avec un nombre de collaborateurs de technologie.
00:40Donc, l'ultime goal est que nous voulons améliorer la médicale education
00:45avec la technologie.
00:47L'ultime goal est l'améliorer la médicale care et aussi l'améliorer la patiente.
00:54Évidemment, si on a une nouvelle médicale solution,
00:59à Paris, ce sont des nouveaux médicales qui sont lesquels
01:02En ce qui nous a maintenant la ?
01:02Une autre chose est ce qu'on a envoyé un français pour l'Australie.
01:08Une autre chose est que nous envoyé un français pour l'Afrique.
01:14Ase est la question de l'asile.
01:20will prohibit them
01:22from doing anything on the patient.
01:25So
01:26the training will be only focused
01:28on discussion
01:29theoretically. And then
01:32after 2 or 3 days they're going to go
01:34back to their home country and begin
01:36performing the procedures on
01:38real patients.
01:40Does this make sense?
01:41And the human body is so complicated
01:44it takes time and exposure
01:46to learn all about
01:48the efficacies
01:49and overcome all the learning curve.
01:52So how are we going to make
01:54this better?
01:56We try to simulate the real world
01:58situations through AI
02:00and based on real time
02:02real world data. And then we
02:04project it into a virtual
02:05environment so that they can practice
02:08in a safe and controlled
02:10way. VR training
02:12is not a very new thing, right?
02:14It has been here for more than a decade.
02:16But the problem is that we are still using
02:18two controllers. Old school,
02:20right? And it's boring.
02:22We can only do selection
02:23training to know the procedures.
02:26Can we make it more interactive?
02:28And that is what we are doing.
02:30So you can see
02:31now we are in an operation
02:33room and we don't have controllers.
02:36We only have our hands
02:37and real world equipments.
02:39We want to do the interaction
02:41and project it into the virtual world
02:43and also we'll combine this
02:45with 3D printed models.
02:48In the old days
02:49we can only do this
02:50in an animal lab
02:51or a cadaver workshop.
02:53It is costly
02:54and very difficult to set up.
02:56Now with only about 20%
02:58of the course
02:58we can take
02:5980% of the effect
03:01or more
03:02because
03:03we can simulate
03:04different
03:05scenarios.
03:06And we also provide
03:08medical communication
03:09training through AI
03:11because communication
03:12is a very essential part
03:14in improving
03:15our medical service.
03:18And we are partnering
03:19with a professor
03:20specialized in
03:21communication.
03:23So through AI
03:25we want to generate
03:26different profiles
03:27and to help them
03:29do identification
03:30about the objects
03:32and smooth out
03:33the workflow.
03:34We are collaborating
03:35with medical schools
03:37and hospitals
03:38and a very important
03:40player in this
03:41ecosystem
03:42is medical device
03:44service provider.
03:46Why?
03:47Because
03:47when we have
03:48a new device
03:49they are advancing
03:51medical solutions
03:53and we have to get
03:54all the professionals
03:55to overcome
03:56the learning curve.
03:58But people never
03:59listen, right?
04:00So let them experience
04:02and let them practice
04:04in a safe way.
04:05We provide
04:06customer solution
04:07and also
04:08SaaS modules.
04:10And the medical
04:11training market
04:11is actually
04:12a huge market
04:14with over
04:15100 USD
04:16billion dollars.
04:19And simulation
04:19training
04:20is actually
04:21the fastest
04:22growing section.
04:24And due to
04:25the booming
04:26medical device market
04:27in China
04:28there is huge
04:30demand
04:30in medical training.
04:32So in
04:33the stage
04:34after COVID
04:35all states
04:36has already
04:37adopted simulated
04:38training
04:39as a clinical
04:40training hours
04:41and the anomalous
04:42studies showing
04:43its effect.
04:45So
04:45there is a better
04:46option
04:47with lower course
04:49and also
04:49higher efficacy.
04:52and a good
04:54medical training
04:54is actually
04:55not just
04:56medicine
04:57knowledge
04:57it's a
04:58combination
04:59of three
04:59elements.
05:00One
05:01is medicine
05:02two
05:03is education
05:04and three
05:06is technology.
05:07We have
05:08created a team
05:09of all
05:10combinations
05:11and growing
05:12into a
05:12passionate team
05:13making a better
05:14future for our
05:15medical training.
05:16So we
05:17started with
05:18about only
05:1910,000
05:20European dollars
05:22and now
05:23developing
05:23into a team
05:24of more
05:25than five
05:25people
05:26and now
05:26we are
05:27ready to
05:27take the
05:27next step.
05:28We are
05:28welcoming
05:29investors
05:29and also
05:30collaborators
05:31such as
05:32hospitals
05:33and medical
05:34device
05:34companies
05:35to join
05:36hands
05:36in improving
05:37the future
05:38of medical
05:39training.
05:40If you
05:40share the
05:41same goal
05:41with us
05:42please
05:42welcome
05:43to contact
05:44us
05:44at
05:45Wismat.
05:45Thank you.
05:50Hi
05:50thank you
05:50actually
05:51we have
05:51a bit
05:51more time
05:52we can
05:52do some
05:53couple
05:53of
05:53questions
05:56do we
05:56have a
05:57question
05:57from the
05:57audience
05:57in regards
05:58to what
05:58you heard?
06:02No?
06:03Okay
06:04well I will
06:05ask questions
06:06so do you
06:06want to tell us
06:07a little bit
06:07more about
06:08the solution
06:09that you're
06:09offering?
06:10Do you
06:11want any
06:12extra things
06:12you want to
06:12share with
06:13us about
06:13what it is
06:13that you're
06:14doing?
06:14I want
06:15to address
06:16the importance
06:16of medical
06:17training
06:18because it's
06:19often neglected
06:20the importance
06:22of medical
06:23training is
06:23to help us
06:24to deliver
06:25the essential
06:26part of
06:26best practice
06:27which is
06:29to safeguard
06:29the patients
06:30because
06:31during an
06:32operation
06:33anything can
06:34happen
06:34right?
06:35There's
06:36emergency
06:36or accidentally
06:38we drop
06:38something
06:39or punch
06:40into the
06:41heart
06:41that always
06:42happens
06:43in the
06:43real world
06:43actually
06:44and in
06:46a few
06:46seconds
06:46a very
06:48small
06:48decision
06:49can cause
06:49us a
06:50life
06:50right?
06:52I have
06:52a friend
06:53who's a
06:53brain
06:53surgeon
06:53and he
06:54tells me
06:54that that's
06:55particularly
06:55a risk
06:57yes
06:57yeah
06:58so it's
06:59too costly
06:59for us
07:00so even
07:01one life
07:02is very
07:03valuable
07:04for us
07:05and for
07:06the family
07:06right?
07:07so
07:09why we are
07:09doing this
07:10is because
07:13I have
07:14experience
07:16real case
07:17yeah
07:18seeing people
07:19pass away
07:19during
07:20procedures
07:23so that's
07:24why I
07:24founded
07:25this company
07:27and I
07:27want to
07:29save
07:29more life
07:30thank you
07:31this is
07:31wonderful
07:31well there
07:32you go
07:32and that
07:33very clearly
07:34demonstrated
07:35is why
07:35she's
07:36building
07:36this solution
07:37and I wish
07:38you all the
07:38best
07:38and thank
07:39you very
07:39much
07:40so ladies
07:40and gentlemen
07:41Visman
07:43Crystal
07:44from Visman
07:46thank you
07:47thank you
07:47very much
07:50all right
07:58so let's
07:58move on
08:00we have
08:01a gentleman
08:02by the name
08:03of Simon
08:03from a company
08:05called
08:05AI Stroke
08:06and so
08:08AI Stroke
08:10is redefining
08:11stroke emergencies
08:13with a mobile
08:15app that enables
08:16real time
08:16detection
08:17so that sounds
08:18like something
08:19that is going
08:19to be very
08:20useful indeed
08:21here to tell
08:22you more
08:22is Simon
08:23from AI Stroke
08:36hi everyone
08:37my name is
08:39Simon
08:39and I'm
08:40the chief
08:40business
08:40officer
08:41of AI Stroke
08:46I don't know
08:47if anyone
08:48know anything
08:49about stroke
08:50but
08:50in our company
08:51we're focusing
08:52on using AI
08:53to diagnose
08:54stroke early
08:57and I'm
08:57waiting for my
08:58slides
09:00here we go
09:07and this is
09:08also me
09:08on the left
09:09of my grandpa
09:11I don't know
09:12if anyone
09:12close to you
09:13any of your
09:14loved ones
09:14ever had a stroke
09:15but in my case
09:17actually both
09:18of my grandpa
09:19passed away
09:19from stroke
09:20so it has
09:21always been
09:21an issue
09:22that's very
09:22close to my
09:23heart
09:24but I also
09:25want to say
09:25that you know
09:26it's not an
09:27issue that is
09:27only about my
09:29family right
09:29stroke is
09:30actually the
09:31second leading
09:31cause of death
09:32and disability
09:33in the world
09:34so that means
09:35one in four
09:37of us
09:37sitting here
09:38will eventually
09:39have a stroke
09:40at some point
09:40in our lifetime
09:42and my grandpa
09:43passed away
09:44because they
09:45arrived stroke
09:46center too late
09:47because there's
09:48one thing you
09:49need to remember
09:49of this session
09:50is that
09:51when you have
09:52a stroke
09:52you only have
09:53four and a half
09:54hours to get
09:54treatment
09:55if you arrive
09:56after that
09:57treatment window
09:58the neurologist
09:59will just tell you
10:00I'm sorry
10:01but you arrived
10:01too late
10:02we cannot treat
10:03you
10:04and that's
10:05what we are
10:05focusing on
10:06at AI Stroke
10:09and actually
10:09less than half
10:11of us
10:12can recognize
10:13the stroke
10:13symptom
10:13and even
10:14on the ambulance
10:16more than
10:17a third
10:17of stroke
10:18cases
10:18is actually
10:19misdiagnosed
10:20and when
10:21it's misdiagnosed
10:22that means
10:22delay
10:22that means
10:23that around
10:2550%
10:26of the people
10:26in the world
10:28sorry
10:2850% of the people
10:30in the US
10:30actually miss
10:31that treatment
10:32window
10:33and that's
10:34actually the number
10:34in rich countries
10:35right
10:35in France
10:36in the US
10:36but if we look
10:38at the whole world
10:38actually more than
10:3995% of the people
10:41when you have a stroke
10:42you miss
10:42your treatment window
10:43and at AI Stroke
10:45we are trying
10:46to change
10:46that number
10:47we are trying
10:48to reduce
10:48that number
10:50in the past
10:51two years
10:51we have built
10:52the world's biggest
10:53data set
10:54of acute stroke patients
10:55we record them
10:56on video
10:57in the data set
10:58inside
10:59we have
10:5920,000 videos
11:01and 6 million images
11:03and that's
11:04the foundation
11:04we're using
11:05to train our AI
11:06to recognize stroke
11:08and our goal
11:09is very simple
11:10we want to reduce
11:11the time
11:12to treatment
11:12by 90 minutes
11:15and the impact
11:16as you can imagine
11:17it's going to be huge
11:18in terms of lives saved
11:19in terms of how much money
11:21we can save
11:21for the healthcare system
11:23and also how many
11:24societal value
11:25generated
11:25because people won't
11:27get disabled
11:27by stroke
11:28and I think that's why
11:31maybe we're awarded
11:32Tech for Change
11:32this year
11:33at Viva Tech
11:34and at yesterday
11:35we also won
11:36the grand prize
11:37at China Pavilion
11:38so how does our AI work
11:40is actually very simple
11:41it's just an application
11:43you can use
11:44on any smartphone
11:45and you don't even need
11:46internet to operate it
11:47but on the other side
11:48it's also quite complicated
11:50as you can imagine
11:50we are actually
11:52a regulated medical device
11:54and it's based
11:55on a patented technology
11:57that's trained
11:58on the tremendous
11:59data set
11:59I just mentioned
12:00but in reality
12:02when you open our app
12:03so for example
12:04the user is
12:05ambulance personnel
12:06they can just
12:08open the app
12:09film the face
12:10of the patient
12:11and then they film
12:12the two arms
12:13of the patient
12:14and they also ask
12:15the patient
12:15to repeat a couple
12:17of sentences
12:17everything takes
12:18two minutes
12:19and our AI
12:20based on that
12:21will analyze
12:22facial paralysis
12:22your motor disorder
12:24and your speech difficulty
12:25and with that
12:27we are able to tell
12:28are you having a stroke
12:29or not
12:29and when we have
12:31this result
12:32our AI agent
12:34then are able
12:34to choose for you
12:35the best stroke center
12:36to go to
12:37and then even
12:38pre-register
12:39the patient
12:39even book
12:40the CT room
12:41for you
12:41like this
12:42we save
12:42the precious time
12:43that you need
12:44for your brain
12:44to recover
12:46so what's the result
12:47so far
12:48we have already
12:49done a study
12:50with
12:51included
12:524,500
12:54EMS personnel
12:55in France
12:56and this is the result
12:57so we're comparing
12:58our AI
12:59with human's ability
13:00to identify stroke
13:01and as you can see
13:02we're already
13:03so much better
13:04in terms of accuracy
13:06and this is our team
13:07very briefly
13:08I'm the chief business officer
13:10and our CEO
13:12and CTO
13:12actually
13:13are serial entrepreneurs
13:14and they already
13:15had a successful
13:1620 million exit
13:17in their previous company
13:18so good for them
13:19but our CEO
13:20is also in charge
13:21of our AI
13:22so he's also
13:22our AI person
13:24and on the first
13:25person on the screen
13:27he's also actually
13:27very important
13:28he's our
13:30medical advisor
13:31and he's the one
13:32who has recorded
13:33all this fantastic
13:35data set for us
13:36that we are using
13:37to train our AI
13:38and he's still
13:41a practicing
13:42director of a stroke center
13:43he has decades
13:44of experience
13:46working with stroke patients
13:48and here is
13:50our board of scientists
13:52both on the AI side
13:54and also on the
13:55neurology side
13:56and we also have
13:57advisors of course
13:58on the commercial
13:58and regulatory side
14:00as well
14:00and in terms of users
14:02so we
14:02our solution
14:03on the first step
14:04will be used
14:05by ambulance personnel
14:06and the results
14:08will also be plugged
14:08into the emergency room
14:11and the stroke centers
14:12to optimize workflow
14:13inside the centers
14:14and we have already
14:15signed paid pilots
14:16and letter of intent
14:18with our users
14:19and a couple of them
14:20is also in discussion
14:21across the world
14:22in Europe
14:23and in the US
14:25and in our second step
14:27we want to also give
14:29the solution to you and me
14:30and more specifically
14:31stroke survivors
14:32because another tricky
14:34thing about stroke
14:34is that once you have
14:35a stroke
14:36you're very likely
14:37to have another stroke
14:38so it's very critical
14:40to help the stroke survivors
14:42to monitor stroke
14:43happenings
14:44when you're at home
14:45so you can catch it early
14:46and treat it early
14:48and here are some
14:49of the discussions
14:50and partners we have
14:52for example in the US
14:53we are discussing
14:54with Stanford Stroke Center
14:56in Texas Medical Center
14:58and in China too
14:59we're discussing
14:59with the best stroke center
15:00in China
15:01Beijing Tiantan Hospital
15:02in Europe too
15:04we are trying to get
15:05a grant from ESC
15:07so we are now
15:09organizing a consortium
15:10including different
15:11stroke centers
15:12in different countries
15:12to try to build
15:14even bigger data set
15:15to improve our algorithm
15:18in terms of fundraising
15:19we have raised
15:20already 1.4 million so far
15:22and we really want
15:23to thank the French
15:23government
15:24because we won the iLab
15:25and with that
15:26BPI France
15:27also give us
15:27a lot of non-dilutive funding
15:29and at the moment
15:31we're raising our seed round
15:32so it's going to be
15:33a 4 million seed round
15:34and again
15:35we have 1.5 million
15:37non-dilutive
15:38committed by BPI France
15:39so we're actually
15:40essentially only looking
15:41for the rest of the 50%
15:43so if you are an investor
15:44or if you are a neurologist
15:46you just want to join us
15:47to help
15:48to have more stroke patients
15:50diagnosed earlier
15:51to save their lives
15:52please come and talk to me
15:53after the session
15:54thank you so much
16:01okay thank you
16:02yeah so I was going
16:03to ask you a question
16:04exactly are you looking
16:05to raise money
16:06at the moment
16:06and you actually
16:07answered that question
16:08in the last slide
16:08so that's great
16:09that's very very clear
16:10so you're very much
16:12you're in the
16:14active pitching mode
16:15yes
16:16yes yes yes
16:16but you sort of
16:17so you've raised already
16:18your first two rounds
16:19and now you're in
16:20sort of your third round
16:21yeah and even for this round
16:22we already have 50%
16:24so we're now just looking
16:25for the next 50%
16:27so hopefully at Viva Tech
16:28absolutely
16:29where can we find you
16:30so we actually have
16:31two booths
16:32we have two booths
16:33sorry one
16:34with the region
16:36because our headquarter
16:38is actually Montpellier
16:39but also since we win
16:40the award in China
16:41a pavilion
16:42so we also have a booth
16:43in China pavilion
16:44so you can find us
16:45in both locations
16:46okay and both of those
16:47are in this hall
16:47in hall one
16:48exactly
16:48okay so they're close by
16:50you're close by
16:51all right any questions
16:51quickly from the audience
16:53about the tech
16:54about how it's working
16:58or maybe
17:01if there's no question
17:02maybe I just want
17:04to give a bonus point
17:05please yeah please
17:05to the audience
17:06because I mean
17:07what I talked about
17:08maybe it's very interesting
17:09for investors
17:09but maybe not for you
17:10but maybe I give you
17:11one additional tip
17:12just for yourself right
17:14if you want to
17:15recognize stroke
17:16when it happens
17:17maybe it's your grandpa
17:18maybe it's your mom
17:19or father
17:19how can we identify it fast
17:22right
17:22so the golden standard
17:24nowadays
17:24pre-hospital
17:25is a protocol called fast
17:27so you're basically
17:28looking at the arm
17:29you're looking at the face
17:31you're looking at the speech
17:32so if you see your mom's face
17:34looks a bit different
17:34than normal
17:35he has a bit problem
17:36on walking
17:37on holding up the arms
17:38when you ask her a question
17:40she cannot really answer
17:41very clearly
17:42that's the moment
17:43you really call the 911
17:45call the ambulance
17:46to get her to the stroke center
17:47then that might save her
17:49of his life
17:50great
17:51well thank you very much
17:52Simon
17:53thank you
17:53from AI Stroke
17:59all right
18:00so we have one more
18:01for you
18:03these guys go
18:04by the name of Reveal
18:05they're a Canadian company
18:06French Canadian company
18:07and they have built
18:09a machine
18:10to help surgeons
18:12work very precisely
18:13and what I understood
18:15is that they have
18:16in fact
18:16they're using a technology
18:19which has decoded
18:20the language of light
18:21so
18:22here to tell you more
18:23is Reveal
18:37I have the voice
18:39a little bit
18:39because I'm at my
18:41third speech
18:43and I bring my paper
18:45because I have three
18:46speeches to do
18:47with Vivatech
18:50because we are
18:51awarded
18:52for the top five
18:53for Take 4 Change
18:54and I want to thank
18:55Vivatech for that
18:57but today
18:58Bonjour Paris
18:59the city of light
19:02where revolution
19:03were born from
19:04ID
19:04from science
19:06and from people
19:10who believe
19:11in science
19:13maybe tonight
19:15another revolution
19:17begins
19:17because we didn't
19:19come here
19:20just to talk
19:20about innovation
19:21we came to talk
19:24about something
19:25invisible
19:26something powerful
19:28the light
19:29what if
19:32if the light
19:33can communicate
19:34more than you think
19:37what if
19:38if
19:38the light
19:39could detect
19:40something
19:40what if
19:42the light
19:42could protect
19:43and what if
19:44get it by AI
19:46it could save
19:47your life
19:51at Reveal
19:52sorry my voice
19:54without the light
19:55to speak
19:56not a human language
19:57but a spectral
19:59signatures
20:00the unique
20:01molecular fingerprint
20:03like a
20:04superman vision
20:05all most of
20:07people
20:07have a dream
20:08to see
20:09like a superman
20:10or maybe a
20:10Tony Stark
20:11a Tony Stark
20:12tech
20:12the unique
20:13molecular fingerprint
20:14of every drop
20:16of fluid
20:17every cells
20:19every breath
20:20and our AI
20:21listen
20:23decode
20:24and learn
20:25no poetry
20:27anymore
20:27but something
20:28just as beautiful
20:29as a signal
20:30that say
20:32this cells
20:33is healthy
20:34this one
20:36is cancer
20:37this is
20:39not an imagination
20:41this is reality
20:43built in Canada
20:45for the world
20:48its cells
20:49start with
20:49the Raman
20:50effect
20:51probably some people
20:52know what is
20:53the Raman
20:53spectroscopy
20:54it's a whole
20:55technology
20:56came
20:57with the
20:58Nobel Prize
20:59back years
20:59in 1929
21:03we used
21:04the laser
21:04to read
21:06the vibration
21:07of the molecule
21:08like reading
21:09the DNA
21:11instantly
21:13but it was
21:14too complex
21:15too slow
21:15too fragile
21:16until we combine
21:18with the things
21:19that we are
21:19really here
21:21the AI
21:22then it became
21:24something else
21:25a tool
21:26that see
21:26what human
21:28cannot
21:28a tool
21:30see the
21:31molecular change
21:32in real
21:33in real time
21:34in living
21:36people
21:36in the
21:37operation room
21:38probably in
21:40space
21:40we didn't
21:42stop there
21:43the combination
21:45of the Raman
21:46and AI
21:47give us
21:48a boundless
21:48detection
21:49of power
21:49we're seeing
21:51infection
21:51inflammation
21:52food quality
21:56drug interaction
21:58no lab
21:59no delay
22:01no guesswork
22:02no human bias
22:03one single
22:05sensors
22:06unlocking
22:07a hundred
22:08of possibility
22:09and it's all
22:10begin
22:11one
22:11tragic
22:12true
22:13even
22:14the best
22:15surgeon
22:16can see
22:17probably
22:19nobody
22:20understand
22:21that
22:22but
22:22our
22:23surgeons
22:23are
22:25completely
22:25blind
22:26even
22:27in
22:272025
22:31he
22:32rely on
22:33scans
22:34early
22:35before
22:36already
22:37outdated
22:38they operate
22:39on hope
22:40on feeling
22:41they operate
22:43on instinct
22:43and also
22:44on expertise
22:45but the margin
22:47are small
22:48the price
22:49of error
22:50is high
22:51the reason
22:52why we start
22:53in the brain
22:53cancer
22:53the most
22:54delicate organ
22:55the most
22:56complicated
22:57tumors
22:58and the most
22:59rootless tumors
23:03that's why
23:0420 to 30
23:06percent of
23:06brain tumors
23:07grow back
23:08and probably
23:09almost
23:10of another
23:11other cancers
23:12that's why
23:13patients undergo
23:14two
23:15three
23:15four times
23:16in the
23:17operation rooms
23:18and that's
23:19why we built
23:20reveal century
23:22this is their
23:24first
23:24machine
23:26medical device
23:27used it
23:28in operation
23:29rooms
23:29to give
23:30the eyes
23:31to the
23:31surgeon
23:32the show
23:34that he
23:34showed the
23:34tumors
23:35as he's
23:36really
23:36at the
23:37molecular
23:37level
23:38where it
23:39starts
23:39where it
23:40stands
23:41in real
23:42time
23:42during the
23:43surgery
23:45reveal century
23:46gives
23:47surgeon
23:48a six
23:48senses
23:50no
23:51die
23:51no
23:52guessing
23:52and no
23:54delay
23:54in real
23:55time
23:57FDA
23:58name
24:00breakthrough
24:01device
24:02designation
24:04it's already
24:05used in
24:06hospital
24:06across three
24:07continents
24:07on research
24:08surgeon
24:10love it
24:10patient
24:11trust it
24:11and for the
24:13first time
24:13they can see
24:15was
24:16uns
24:16invisible
24:17a safe
24:18line between
24:19what most
24:20be removed
24:20and what
24:21most
24:22we preserve
24:23and that
24:25was just
24:25that's not
24:26just our
24:27first
24:27breakthrough
24:30let's
24:31talk to
24:31woman in
24:33the place
24:3330%
24:36of woman
24:36here
24:38who received
24:39cancer
24:40breast
24:41diagnostic
24:41will need
24:43a second
24:44operation
24:45why
24:46because
24:48the
24:48surgeon
24:48can
24:49always
24:49see
24:51what's
24:51left
24:52with
24:53reveal
24:54that's
24:55a
24:55completely
24:55change
24:56one
24:57surgery
24:58one
24:59moment
24:59one
25:00life
25:00changing
25:01outcome
25:02we believe
25:04a woman
25:04should never
25:05have to
25:06choose
25:06between
25:07mutilation
25:08and
25:09life
25:11we believe
25:12in this
25:14technology
25:14we protect
25:16what matter
25:16most
25:17the body
25:17and
25:18dignity
25:22but we
25:24ask
25:24what if
25:25this
25:27technology
25:27can do
25:29more
25:29and most
25:30easy
25:30thing
25:32what if
25:33we could
25:34detect
25:34disease
25:35in
25:35fluid
25:36early
25:37stage
25:38we build
25:40rival
25:41biofluid
25:42OS
25:43same
25:45machine
25:46different
25:46packaging
25:47same
25:48platform
25:49a portable
25:50real-time
25:51system
25:51that reads
25:52saliva
25:52blood
25:53and
25:54urine
25:55developing
25:56during
25:57the
25:57covid
25:57pandemic
25:58it's
26:00beat
26:00any
26:00rapid
26:01tests
26:01in
26:01accuracy
26:02cost
26:03and
26:03speed
26:04and
26:05now
26:05we
26:05detect
26:06thing
26:06listen
26:07carefully
26:08what I
26:08tell
26:08you
26:09we
26:10detecting
26:11lung
26:11cancer
26:12stage
26:13one
26:14in
26:1650
26:16maybe
26:17under
26:17a minute
26:21during
26:22we
26:23found
26:23also
26:25in our
26:26research
26:26in a couple
26:27of weeks
26:28we will
26:28receive
26:29the
26:30response
26:31on that
26:33another
26:34three
26:35other
26:35cancers
26:37but we
26:38will have
26:38published
26:39on that
26:39and you
26:40will see
26:40in
26:41major
26:41news
26:43we
26:44found
26:44flu
26:45RSV
26:46also
26:47missile
26:47and now
26:49we
26:49detecting
26:49a lot
26:51of
26:51other
26:51things
26:52from
26:52saliva
26:52this is
26:54not a
26:54diagnostic
26:56that's
26:56early
26:57interventions
26:59and maybe
27:02it's a
27:03form
27:04of
27:04cure
27:04did you
27:06know
27:06the
27:06prognostic
27:07of a
27:07lung
27:07cancer
27:08if you
27:08find it
27:09at 95%
27:12it's
27:13very
27:13special
27:16but beyond
27:17medicine
27:17we
27:18wonder
27:19if
27:20work in
27:20surgery
27:21if it
27:22work in
27:22surgery
27:23probably
27:23work
27:24anywhere
27:24what else
27:26can do
27:27so we
27:28test
27:28rebellion
27:29agriculture
27:29in
27:31food
27:32perfume
27:34imagine
27:35the reason
27:37why a
27:37perfume
27:38smells
27:38great
27:39imagine
27:40why
27:41champagnes
27:42have a
27:42great
27:42taste
27:43the reason
27:44why
27:44a molecular
27:46reason
27:46this is
27:48what we
27:48found
27:49we found
27:50fruit in
27:51champagne
27:52nitrate
27:53in drinking
27:53waters
27:54also
27:55sulfate
27:56if you
27:57know
27:57what
27:57is
27:57contaminated
27:58the lake
27:59and
28:00water
28:01sulfate
28:02it's a
28:02big
28:02concern
28:03and we
28:04are
28:04one of
28:04the
28:04first
28:05that
28:06we
28:06detect
28:06in
28:06real
28:07time
28:07doping
28:08in
28:09athlete
28:09we
28:10are
28:10working
28:11with
28:11government
28:12we're
28:12exploring
28:13also
28:13security
28:14we're
28:15protecting
28:16human
28:17health
28:17at
28:18every
28:18scale
28:18from
28:19hospital
28:19maybe
28:20to
28:21refugee
28:21camp
28:22even
28:23space
28:25but
28:26reveal
28:26is not
28:27just
28:27a
28:28device
28:28this
28:29is
28:29the
28:29most
28:29important
28:30thing
28:31it's
28:32a
28:32data
28:32platform
28:33every
28:34scan
28:35becomes
28:35a part
28:36of
28:36intelligence
28:37system
28:38we're
28:39building
28:40the
28:40google
28:41map
28:41of
28:41molecule
28:42sorry
28:43for
28:43although
28:44I
28:44name
28:45google
28:45but
28:45maybe
28:46you
28:46understand
28:46what
28:46I
28:47mean
28:47a
28:48real
28:48time
28:49atlas
28:49of
28:50human
28:51and
28:52environmental
28:52health
28:53no
28:55black
28:55oh
28:56okay
28:57no
28:58black
28:58box
28:59say hi
28:59but
28:59explainate
29:00transparency
29:01regulatory
29:02ready
29:02science
29:03for
29:03doctor
29:04for
29:04pharma
29:04for
29:05public
29:05health
29:05it's
29:06real
29:07it's
29:07ready
29:07it's
29:08deployable
29:09over
29:10200
29:12back
29:13head
29:13studies
29:14publishing
29:14in
29:15nature
29:15more
29:16than
29:16three
29:17times
29:17spies
29:1720,000
29:19measurements
29:1912
29:20hospitals
29:2112
29:22million
29:22already
29:22invested
29:23in
29:24medtech
29:25and
29:25FDA
29:26breakthrough
29:26device
29:28I'm
29:29Alexandre
29:29Canadian
29:30at
29:30birth
29:31my
29:31French
29:32is
29:32worse
29:32for
29:33my
29:33French
29:33Parisian
29:34and
29:35worse
29:35in
29:36English
29:36also
29:36because
29:37I'm
29:37from
29:37Quebec
29:38and
29:39fully
29:39committed
29:39to
29:40bring
29:40reveal
29:40to
29:40the
29:40people
29:41who
29:41need
29:41most
29:41because
29:42tonight
29:42we
29:43didn't
29:43just
29:43turn on
29:44the
29:44light
29:44turn
29:45on
29:45the
29:45light
29:45we
29:46bringing
29:46it
29:46to
29:46the
29:46world
29:47thank
29:47you
29:49all right
29:50thank you
29:50very much
29:51ladies and
29:51gentlemen
29:52reveal
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