What is the main focus of Dr. Jeanette's presentation?

The focus is on the impact of artificial intelligence in neurology, specifically for stroke and epilepsy.

How has the interest in AI publications changed recently?

Publications in AI have been increasing rapidly, particularly from technology companies like Facebook and Google.

What are the two major applications of AI in neurology discussed?

Stroke management and the development of biomarkers for diseases like Alzheimer's.

Why is AI considered a technology enabler?

AI is seen as a general purpose technology that can be applied across various fields, not limited to one specific area.

How does AI help reduce latency in stroke care?

AI applications in stroke care can decrease the time to treatment, potentially saving lives and reducing disability.

Artificial Intelligence in Neurological Diagnosis & Management | 18th Neurology Certificate Course

00:42:04

https://www.youtube.com/watch?v=Kmrt4T-hwVM

Resumen

TLDRDr. Jeanette's presentation at the neurology certificate course addresses the rapidly evolving intersection of artificial intelligence (AI) and neurology. She emphasizes how AI can significantly impact the diagnosis and treatment of neurological disorders, particularly in stroke care and epilepsy. The lecture highlights the urgent need for healthcare professionals to understand AI as an essential part of their practice. Key topics covered include the acceleration of AI research publications, the importance of high-quality data, and the potential of AI to reduce latency in care delivery, ultimately improving patient outcomes. Dr. Jeanette also discusses how AI can develop new biomarkers for diseases like Alzheimer's disease and stresses the need for continuous learning environments in AI applications within medicine. Overall, she advocates for the integration of AI in clinical settings to enhance decision-making and prediction capabilities while maintaining the crucial role of physicians.

Para llevar

🤖 AI is revolutionizing neurology and clinical practice.
📈 AI publications are increasing rapidly, led by tech companies.
🧠 AI can reduce treatment latency in stroke care.
🔬 High-quality data is essential for effective AI applications.
💡 Understanding AI is now as critical as learning medical biochemistry.

Cronología

00:00:00 - 00:05:00
Jeanette, a neurocritical care specialist, discusses the role of artificial intelligence (AI) in neurological disorders during the 18th neurology certificate course at Aahan University Hospital. She shares her background and ongoing work in the field, emphasizing the importance of understanding clinically relevant AI concepts.
00:05:00 - 00:10:00
AI is rapidly changing healthcare by affecting algorithms that can influence billions of people globally. Jeanette highlights the significant increase in AI publications, particularly in neurology, stressing that academia is lagging behind tech companies like Facebook and Google.
00:10:00 - 00:15:00
The rise in AI publications correlates with the availability of high-quality data, particularly in neurodegenerative disorders like Alzheimer's and Parkinson's. Furthermore, Jeanette cites the increasing relevance of AI in stroke care, emphasizing the importance of real-world applications in contrast to academia.
00:15:00 - 00:20:00
AI development in healthcare is accelerating due to reduced costs of compute power and faster data storage. Jeanette notes that understanding AI concepts becomes essential for healthcare providers to improve treatment quality and reduce latency in care delivery, especially for conditions like stroke.
00:20:00 - 00:25:00
Jeanette emphasizes that it is crucial to understand AI as a technology enabler rather than just a tool. She stresses the need for medical professionals to learn about AI, likening its importance to foundational medical knowledge and asserting that its application will impact patient care significantly.
00:25:00 - 00:30:00
There are fundamental differences in reasoning methods between AI and traditional methods. AI, particularly machine learning and deep learning, relies on patterns and examples rather than rules and instructions, leading to more efficient hypotheses and discoveries in clinical practice.
00:30:00 - 00:35:00
Statistics and machine learning serve different purposes; statistics identify relationships while AI focuses on predictions. Jeanette underscores the shift from reactive disease management to proactive prediction and prevention through AI applications in healthcare.
00:35:00 - 00:42:04
Artificial intelligence is a critical tool that, while not replacing physicians, will aid in augmenting their capabilities. Jeanette forecasts a future where physicians using AI will outperform those who do not, advocating for an integration of AI in daily medical practices.

Mapa mental

Vídeo de preguntas y respuestas

What is the main focus of Dr. Jeanette's presentation?
The focus is on the impact of artificial intelligence in neurology, specifically for stroke and epilepsy.
How has the interest in AI publications changed recently?
Publications in AI have been increasing rapidly, particularly from technology companies like Facebook and Google.
What are the two major applications of AI in neurology discussed?
Stroke management and the development of biomarkers for diseases like Alzheimer's.
Why is AI considered a technology enabler?
AI is seen as a general purpose technology that can be applied across various fields, not limited to one specific area.
How does AI help reduce latency in stroke care?
AI applications in stroke care can decrease the time to treatment, potentially saving lives and reducing disability.

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Subtítulos

Desplazamiento automático:

00:00:00
hi this is jeanette neurocritical care
00:00:01
stroke and epilepsy specialist today i'm
00:00:03
going to be presenting an artificial
00:00:04
intelligence and neurological disorders
00:00:06
i'm very grateful to be invited to speak
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on the prestigious course which is the
00:00:11
18th neurology certificate course from
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aahan university hospital being medical
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advisor consultation for many different
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companies and on artificial intelligence
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that i can't list over here but these
00:00:22
are generally where my current positions
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are in terms of
00:00:27
employment and contraction and some of
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the independent work that i'm doing in
00:00:31
the field itself
00:00:32
now this is a very big topic and i
00:00:35
cannot boil an ocean
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what that term means is basically that
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this is a vast ocean in which we have to
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get into so the objective is basically
00:00:43
to get as much information as you need
00:00:45
in terms of clinically relevant
00:00:47
artificial intelligence and getting the
00:00:49
basic concepts around it and then you
00:00:51
will improve upon it more and more your
00:00:53
knowledge in terms of looking at our
00:00:55
artificial intelligence articles youtube
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videos etc and going from there my
00:01:00
articles my videos are more and more
00:01:02
going to be concentrating on artificial
00:01:04
intelligence so make sure you follow
00:01:06
me in terms of my youtube channel my
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blog and of course neurology pocketbook
00:01:11
as well
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so here's the issue
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change was always changing right it's
00:01:16
never been that issue right i mean at
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the end of the day the
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acceleration of change itself has been
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accelerating for the past decade what
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artificial intelligence is that it
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changed from logarithmic to algorithmic
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change what's the difference the
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difference is that if there's an
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algorithmic change let's say in terms of
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youtube algorithm it affects two billion
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people tick tock three billion people
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facebook three and a half billion people
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it's ridiculous the amount of change
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that you can produce when you change an
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algorithm i mean we always have heard
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this right i mean
00:01:50
this has been an alumni balls code that
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we keep quoting in pakistan especially
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for a long long time but believe it or
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not the change itself has changed
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interestingly this uh 1922 22 exactly a
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hundred years ago allama iqbal was given
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the title of sir so interestingly we
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have been a 100 year journey and we are
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still talking about changes change and
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believe it or not this time it's very
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significant
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now publications in artificial
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intelligence have been increasing at an
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alarming rate as a matter of fact the
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peer-reviewed applications there were
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publications that were coming in
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artificial intelligence is on a rise
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constantly and more importantly start
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just coming from the academics believe
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it or not more and more publications
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that i read personally are coming from
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facebook and google because those are
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the technology papers that i have to
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make myself aware of because more and
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more changes that these companies are
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coming up with and they are doing real
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good publication and sometimes it's open
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source open access so first of all i
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need to know them personally but just
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letting you know that academia is truly
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falling behind in terms of
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research publications and artificial
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intelligence so if you want to follow
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that technological curve you need to
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follow corporate publications as well as
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academic publications as well
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as far as neurology is concerned as you
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can see every single year there is more
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and more increase in terms of
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publications and neurology these are
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only specific clinical application
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neurology publications that we are
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talking about and you can see that from
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2018 to 2020 there is clearly doubling
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every year in terms of publications and
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neurology is concerned all the other
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places as far as you know
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sub-specialties are concerned there has
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been publications general publications
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radiology is one of the top
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publications where you would see
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artificial intelligence being applied to
00:03:39
however cardiology and neurology are
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neck to neck and if you see covet 19 of
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course was a little higher in terms of
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publications which is probably going to
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fall off now given that we are going
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over the hump of that so artificial
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intelligence application has been used
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in every freaking sub-specialty and
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neurology is not behind as a matter of
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fact neurology is right up at the level
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of cardiology in terms of publications
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as well
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now the publications are divided into
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five major categories and the issue here
00:04:09
is that the why is that those areas
00:04:12
being concentrated on it has nothing to
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do with clinical application it has
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everything to do with the availability
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of high quality data
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look alzheimer's parkinson's which are
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the neurodegenerative and dementia
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disorders have had multiple years of
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funding from the government in terms of
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fda nih and ihss and inds all these
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major federal funding has been going
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into dementia because alzheimer's has
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been a big problem when we need newer
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newer solutions
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all those data that has been collected
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in last 10 years from imaging biomarkers
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everything can be churned up through
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artificial intelligence better faster
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because we have availability of high
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quality data however the application the
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true world application in artificial
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intelligence is in stroke that's we're
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going to come into in the next few
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slides so you have to distinguish
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between publications in terms of what
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academia is moving towards and real life
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applications so the reasoning behind it
00:05:09
is not just the publications itself the
00:05:10
reasoning behind it is basically
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availability of high quality data
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so
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what it tells me everything is that ai
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is developing super fast as a matter of
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fact when you're going to look at it in
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terms of both technological aspects and
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in terms of clinical applications we are
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seeing more and more applications that
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are getting fda
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i'm not going to use the word approval
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so that's another big thing because fda
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granted fda access fda listed and fda
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approved however for now we're going to
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see that ai is developing fast its
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adoption has become faster as a matter
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of fact more and more industry leaders
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and more and more experts are
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proposing that especially in healthcare
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artificial intelligence should be the
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way to go especially with hims22 if you
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look at the news that came from there
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more and more leaders are going to be
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open to adopting artificial intelligence
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now why now i think there are multiple
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reasons and this is a whole chart and we
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can go into but given the time i'm going
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to just talk about two things number one
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is
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the key issue is compute power it has
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become significantly cheaper and it's
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significantly accessible in terms of
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compute power because we can do compute
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over the cloud and even our devices are
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getting much and much powerful in terms
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of compute second big thing is data and
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not just data in terms of slow hard
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drive spinning drives data hard
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solid state drive which is the flash
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drives and the reason behind is that we
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need faster drives sometimes so that the
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data can go in and out pretty fast
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within the computer engines that is
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decreasing in cost so two major storage
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and compute power is decreasing in cost
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significantly so the accessibility for
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the barrier to entry has gone
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significantly low for these studies to
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come up and therefore more and more
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studies are being done and more and more
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applications are being done on
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artificial intelligence before we get
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into it i always always remember that we
00:07:03
need to talk about why is it important
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you know you can already apply
00:07:07
artificial intelligence in any data
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right i mean let's say i'm gonna do uh
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pull out some of the tweet data from
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myself and then put artificial
00:07:15
intelligence great right i mean we can
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do that but the more important thing is
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to always find out why we want to do
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something especially in terms of
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technological applications so the key
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issue with a neurology is what
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latency of care time is brain so one of
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the things that we want to apply
00:07:31
artificial intelligence in situations
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where we can decrease the time for
00:07:35
treatment stroke hemorrhage subarachnoid
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hemorrhage etc the second big issue is
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that we don't have a troponin like i
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mean that would be fantastic right if
00:07:43
you can get a blood test for alzheimer's
00:07:45
we can't if you had that that would be
00:07:47
that would solve so many issues because
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we can have early detection of
00:07:50
alzheimer's and then early intervention
00:07:52
of the alzheimer's we can't do that so
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we need new
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significantly new biomarkers in terms of
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imaging in terms of mci clinical
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biomarkers and in terms of genetic
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biomarkers and meaning we probably need
00:08:03
to combine them so the question is that
00:08:05
we need to make sure that we have new
00:08:07
forms of biomarkers that could only be
00:08:10
achieved by large data
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churned by artificial intelligence we're
00:08:14
going to continue to have lack of
00:08:15
neurologist i mean pakistan has a
00:08:17
significant lack of neurologist then on
00:08:19
top of that we also have a long tail of
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these patients that are chronically
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going to live with neurological
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disorders either parkinson's alzheimer's
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stroke etc epilepsy all of these things
00:08:29
we're going to significantly see a
00:08:31
growing population of older and older
00:08:33
people around the world and they are all
00:08:36
going to get you know basically
00:08:39
neurological problems and we need to
00:08:40
help them and this is what i keep
00:08:42
telling cardiology of my cardiology
00:08:44
folks is that thank you for making sure
00:08:46
that people are alive so i can take care
00:08:47
of their neurological problems because
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that was the last
00:08:50
century to be honest with you where
00:08:52
cardiology was at the boom in which
00:08:54
heart problems were the issue when
00:08:55
people were constantly getting heart
00:08:57
attacks and dying from it but in their
00:08:58
lot now they're living well
00:09:01
and unfortunately being afflicted by
00:09:03
neurological disorders
00:09:05
now before i get into the direct
00:09:07
applications of neurology let me just
00:09:09
give you some conceptual basics of
00:09:10
artificial intelligence first and
00:09:12
foremost you need to understand that
00:09:13
artificial intelligence is not a
00:09:15
technology it's a technology enabler
00:09:18
what that means is that any type of data
00:09:20
that you're going to generate in either
00:09:22
through blockchain dna sequences
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internet of things healthcare iot
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autonomous mobiles robotics any
00:09:29
situation in which you're going to
00:09:31
generate data and you need to have run
00:09:33
assimilations of multiple possibilities
00:09:36
you're going to need artificial
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intelligence
00:09:38
so this is considered what we call
00:09:40
general purpose technology it is not
00:09:42
considered one technology so therefore
00:09:45
when you have a technology like general
00:09:46
purpose technology like artificial
00:09:48
intelligence you do not compare it with
00:09:50
just dna sequences because that's one
00:09:53
part of technology that cannot be
00:09:54
applied in finance but artificial
00:09:57
intelligence can be applied in finance
00:09:59
healthcare economics education
00:10:01
everything so therefore artificial
00:10:03
intelligence is to be compared with
00:10:05
electricity so its artificial
00:10:07
intelligence is as important as
00:10:09
electricity in terms of its impact
00:10:12
because it's a general purpose
00:10:14
technology and that's why there's such a
00:10:16
huge buzz and everything i mean there's
00:10:18
a lot of buzz so don't get into it but
00:10:19
at the end of the day that's where the
00:10:21
real difference is in terms of just
00:10:24
being a technology or being a technology
00:10:26
enabler
00:10:27
now
00:10:28
second thing that you have to understand
00:10:30
is like the first question why do i need
00:10:31
to know ai i'm going to give you many
00:10:33
examples of artificial intelligence
00:10:34
being used in clinical impact
00:10:37
exactly the same way that you have
00:10:39
learned
00:10:40
mechanism of actions of drugs all your
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life right like this is topo mermaid
00:10:45
this is kapra this is a mechanism action
00:10:47
this is the half-life this is what that
00:10:49
is a tool that you use to help your
00:10:51
patients exactly in the same way you're
00:10:54
going to be using artificial
00:10:55
intelligence in the future and exactly
00:10:57
in the same way you need to understand
00:10:58
some basic some mechanism of action for
00:11:01
artificial intelligence to make sure
00:11:03
that you are appropriately applying
00:11:05
those tools for your patient care
00:11:07
so do not think that artificial
00:11:08
intelligence is something you do not
00:11:10
need to know it is now as important as
00:11:12
learning biochemistry in pre-med and
00:11:15
then biochemistry in med school or
00:11:16
anatomy or physiology there should be
00:11:18
courses on data sciences and artificial
00:11:20
intelligence in medicine this is not the
00:11:22
time i can give you some more big
00:11:24
references on where
00:11:25
at least universities in u.s are going
00:11:27
if you want to check out northwestern
00:11:28
university just added a complete course
00:11:30
on data sciences and artificial
00:11:32
intelligence in their medical school
00:11:33
curriculum the reason is this you are
00:11:36
going to be taking care of these
00:11:37
patients using these tools so you need
00:11:40
to make sure you understand some basics
00:11:42
at least enough that you
00:11:44
currently know what the mechanism of
00:11:46
action for some medicines are and choose
00:11:48
accordingly which medication you're
00:11:49
going to use and which you're not going
00:11:50
to use exactly in the same way you have
00:11:52
to start using artificial intelligence
00:11:54
which software stack you're going to use
00:11:56
and which you're not going to use to
00:11:57
help this particular patient so that's
00:12:00
why it is extremely important and this
00:12:01
is your role as a physician to
00:12:03
understand this in the future and the
00:12:05
future is not that far as i told you
00:12:07
before now when you're talking about
00:12:08
artificial intelligence what we're
00:12:09
talking about is machine learning and
00:12:11
that's a lot of good term that i use the
00:12:13
word artificial intelligence because
00:12:14
that's a general purpose technology and
00:12:16
there are different ways to globally
00:12:17
tell something and the other way is not
00:12:19
to say generally the best term to use is
00:12:21
machine learning and the one of the key
00:12:23
issues with the machine one of the
00:12:25
subsets in machine learning is deep
00:12:26
learning so let's talk about the the one
00:12:29
two things we're going to be talking
00:12:30
about is machine learning and deep
00:12:31
learning machine learning is basically
00:12:33
that when a machine basically learns
00:12:35
from examples and deep learning is
00:12:37
basically in which we are not giving too
00:12:39
much labeled data and more and more
00:12:41
excess exceptionally large amount of
00:12:44
data is added in terms of deep learning
00:12:47
and therefore when you are trying to
00:12:48
explain so colloquially we use the term
00:12:51
artificial intelligence which is to be
00:12:52
honest with you not the right term the
00:12:54
term we should be using is machine
00:12:55
learning and whenever we are referring
00:12:57
to artificial intelligence especially in
00:12:58
healthcare we're talking about machine
00:13:00
learning
00:13:00
machine learning has another subset
00:13:02
called deep learning and deep learning
00:13:04
is basically when you have more higher
00:13:06
forms of learning that are compute
00:13:08
intensive and data intensive in that
00:13:10
regard we have to see that when you are
00:13:13
moving from machine learning to deep
00:13:14
learning there's massively increasing
00:13:17
amounts of requirements for data and
00:13:19
massively increasing requirements of
00:13:21
compute power however we lose one key
00:13:24
issue we lose explainability because in
00:13:27
machine learning we are giving them at
00:13:28
least some sort of rules to go back and
00:13:31
then we can explain that how that result
00:13:33
was coming up to but in deep learning
00:13:35
that is not the case let me explain
00:13:37
further
00:13:38
so what is the difference right i mean
00:13:41
what's the freaking hype about in terms
00:13:43
of artificial intelligence we have been
00:13:45
you know have human intelligence for
00:13:46
years and years right so we have been
00:13:48
programming for years like it's been
00:13:50
approximately 50 years of coding and
00:13:53
programming that has been going on how
00:13:54
do we do that human coders said they
00:13:57
put in spit out a lot of instructions
00:13:59
and rules that rules and instructions
00:14:01
basically create a program
00:14:03
something like this in which there's
00:14:04
video uploaded on the youtube etc and
00:14:07
those instructions are basically
00:14:08
packaged and then go into it what's the
00:14:11
difference is that then we no longer
00:14:13
want human to code data codes we
00:14:17
basically do not give them instructions
00:14:19
we give them examples
00:14:21
let me rephrase it
00:14:23
you know there are ten thousand cat
00:14:25
pictures and we can tell them okay do
00:14:27
something about it then the the computer
00:14:29
will never learn because no matter how
00:14:30
many times you have instructions to
00:14:33
divide that from between
00:14:34
dog and cat it just never works this is
00:14:37
a completely new paradigm in which you
00:14:39
do not give instructions to the page to
00:14:42
the computer you give okay here are a
00:14:44
million pictures this is the dog this is
00:14:47
the cat and then learn from it and
00:14:49
here's another 10 000 more where you're
00:14:51
gonna be able to distinguish between the
00:14:54
two sets of data and test them we'll
00:14:56
come to that in a minute so the key
00:14:59
difference is human codes to data code
00:15:02
instructions to examples we give tons
00:15:05
and tons of examples to the computer and
00:15:08
that's how it learns and that's to be
00:15:10
honest with you is the basis of human
00:15:11
learning as well another big difference
00:15:13
is that that reasoning and artificial
00:15:15
intelligence look what we have been
00:15:17
taught about is deductive reasoning
00:15:19
that's how we run virtually clinical
00:15:21
trials that's how we move on in terms of
00:15:24
scientific discovery scientific
00:15:26
validation what do we do we have a
00:15:28
theory that i don't know tevera made uh
00:15:30
helps with the headache then we create
00:15:32
that into a hypothesis then we create
00:15:35
virtual clinical trials divide patients
00:15:36
into two arms we do observations and
00:15:38
then we confirm that that if our
00:15:41
hypothesis was correct or wrong
00:15:44
this is our current methods of thinking
00:15:46
and this is what we call aristotle or
00:15:48
deductive thinking
00:15:50
sherlock or inductive thinking has been
00:15:52
that we don't care we just say there are
00:15:56
tons and tons of millions of you know
00:15:57
records and and here and then just find
00:16:00
out patterns in it and then see with the
00:16:02
patterns if we can create some more
00:16:04
hypothesis and then we create theory
00:16:07
so for example there are a million
00:16:08
epilepsy patients who are on topiramate
00:16:11
and then we saw that they also had some
00:16:12
10 000 of them had migraine and we saw
00:16:15
that those patients were on top pyramid
00:16:16
had a decreased headache frequency
00:16:19
because they were on the
00:16:21
that's artificial intelligence because
00:16:23
you're putting in a lot of data which
00:16:25
there's pattern recognition and it gives
00:16:27
you a theory rather than the other way
00:16:29
around so artificial intelligence is
00:16:31
inductive reasoning artificial
00:16:33
intelligence starts with observation
00:16:35
examples in terms of deductive reasoning
00:16:38
it starts with theory and confirmation
00:16:40
so that's a completely different way of
00:16:41
looking at things and then you will have
00:16:44
to do a complete clinical trials as well
00:16:46
but what ai can do is get you there
00:16:48
faster can get you there smarter and
00:16:50
that's where drug discoveries are being
00:16:52
done through artificial intelligence
00:16:54
that's exactly the pathway that they go
00:16:55
into which was a different pathway for
00:16:57
what we were doing before because we
00:16:59
were now able to select compounds much
00:17:01
faster much easier much smarter to do
00:17:04
for clinical trials through the
00:17:05
inductive reasoning process which uses
00:17:08
which is based artificially intelligence
00:17:10
is based out of
00:17:11
and this is what i was talking about
00:17:12
practically skipping when you have an
00:17:14
original data format you turn it into a
00:17:16
training data set into a testing data
00:17:18
sometimes you do have a validation data
00:17:20
set as well in between to hypertune your
00:17:23
parameters but either way the logic
00:17:25
remains the same that you take a tons
00:17:27
and tons amount of data separate it down
00:17:29
into training data and say okay these
00:17:30
are label data these are cats these are
00:17:32
dogs and then these are unlabeled data
00:17:34
in which you know i'm going to test how
00:17:36
do you perform in identifying those
00:17:39
images the other thing you need to know
00:17:40
is what's the real difference between a
00:17:42
statistics and machine learning you
00:17:44
understood the in reasoning behind
00:17:45
artificial intelligences which is
00:17:47
different than what we have been doing
00:17:48
and that's why it's harder especially
00:17:50
for physicians to get that because we
00:17:52
have been ingrained into clinical
00:17:54
evidence process in a different way
00:17:56
now and then the tools that we have been
00:17:59
using for years and years for clinical
00:18:00
research has been statistics
00:18:03
in statistics the whole goal is what
00:18:05
relationships to understand that if this
00:18:08
particular
00:18:09
situation correlates with this
00:18:11
particular disease smoking to cancer so
00:18:14
it doesn't matter it's still not
00:18:16
causality right we don't know if it's
00:18:18
truly causing right it's it's at the end
00:18:19
of the day it's a relationship that we
00:18:21
have identifying increased risk of
00:18:22
cancers and stroke and in smoking
00:18:24
patients and then we infer from that
00:18:26
particular statistics and that's why
00:18:28
it's very explainable because we know we
00:18:30
have run different kinds of
00:18:33
variations of statistics and then we
00:18:34
came to that particular conclusion
00:18:37
and the the artificial intelligence has
00:18:39
nothing to do with that artificial
00:18:41
intelligence is all about prediction
00:18:43
prediction prediction i'm sitting on a
00:18:45
card table and i want to know what the
00:18:47
next chord is so i can bet something
00:18:50
different that is it
00:18:52
the whole purpose of artificial
00:18:54
intelligence is to predict that's where
00:18:56
the real magic comes in if there's going
00:18:58
to be a financial decline or if there's
00:19:01
going to be this patient is going to
00:19:02
develop stroke in the next 10 years this
00:19:05
patient is going to develop heart attack
00:19:06
in the next five years we have lots and
00:19:08
lots of data we have millions of
00:19:10
examples and we have seen different
00:19:12
areas in which these patients perform
00:19:14
differently and now we can through a
00:19:17
significantly precise way predict
00:19:21
where that patient is going to fall in
00:19:23
the next five to 10 years and you can
00:19:25
change the trajectory that's the key
00:19:27
thing right we are in always responding
00:19:31
quite frankly most of the time in terms
00:19:33
of disease management and this is the
00:19:36
paradigm shift in terms of
00:19:38
disease management to moving from
00:19:40
treatment to prediction and prevention
00:19:43
so that's the key difference between
00:19:45
statistics and machine learning because
00:19:47
that's where we are going into in terms
00:19:49
of artificial intelligence and it is
00:19:52
truly based on human learning right i
00:19:54
mean if you look at the human learning i
00:19:55
mean if i look at one of the cat what
00:19:57
happens i have the one neuron that goes
00:19:59
into and then connects to different
00:20:00
synapses to my virtual uh visual cortex
00:20:03
and comes back and tells me okay this is
00:20:05
a cat and exactly in the same way
00:20:08
artificial neural networks are a design
00:20:11
it takes one of the images divides into
00:20:13
multiple areas and each single pixel is
00:20:15
sometimes
00:20:17
regulated in terms of what it is and
00:20:19
then eventually you get a complete
00:20:21
classification if this particular cat
00:20:23
scan or mri has an infarction tumor or
00:20:26
hemorrhage so what we're seeing is that
00:20:28
that it is that's why we call it
00:20:30
artificial intelligence because we are
00:20:33
trying to mimic human intelligence and
00:20:35
that's where the beauty of this whole
00:20:37
thing comes in
00:20:38
now
00:20:39
another thing you need to realize as far
00:20:41
as a difference is concerned is that
00:20:44
what is the difference between an
00:20:45
algorithm
00:20:46
and artificial intelligence look we
00:20:49
people don't understand they use the
00:20:50
word algorithm all the time and by the
00:20:52
way we have been using algorithms for 50
00:20:54
years this is a basic algorithm for
00:20:56
hyper neutrinia that we have used for
00:20:59
many many years right i mean i'm
00:21:01
assuming everyone has used that what's
00:21:03
this this is basically a rule-based
00:21:05
algorithm in which what you have done
00:21:07
what you have done is basically you have
00:21:08
sodium for this then you go true or
00:21:10
pseudo and then you go to next step then
00:21:12
you go to the next fork then you get to
00:21:14
the next fork order some studies and
00:21:15
then move forward this has been a
00:21:17
rule-based algorithm and as a matter of
00:21:19
fact to be honest with you medicine runs
00:21:21
on this rule-based algorithms if you
00:21:23
have a book with rule-based algorithms
00:21:24
you can give it to a first-year
00:21:26
medical student and he can get you
00:21:28
somewhere in terms of moving the patient
00:21:30
management forward
00:21:32
what's the difference then when you have
00:21:34
artificial intelligence
00:21:36
you see the rules are not made there are
00:21:38
no instructions
00:21:40
we give a million
00:21:41
records to the patient and tells them
00:21:44
well this is hyponatremia what do you
00:21:45
think it is as far as diagnosis is
00:21:47
concerned with the million examples it
00:21:49
finds out okay if the patient has these
00:21:51
characteristics then it's going to fall
00:21:52
into this bucket these characters fall
00:21:54
into this bucket these characters will
00:21:56
fall into this bucket creates its own
00:21:58
database algorithm and then when you
00:22:00
give another 10 000 new
00:22:02
testing protocol then it can predict
00:22:05
for you
00:22:07
where that patient is going to be
00:22:08
do you understand now i'm hoping that
00:22:10
this circles in because it's hard to put
00:22:13
these concepts into one sort of lecture
00:22:16
so this is the whole idea behind it who
00:22:18
will benefit from loop direct directs
00:22:21
and you're basically going to see okay
00:22:22
the data is going to basically tell you
00:22:25
who is going to do that rather than you
00:22:27
creating a rule-based system now
00:22:30
in the beginning there will be more
00:22:31
hybrid approaches right i mean i'm going
00:22:32
to basically use natural language
00:22:34
processing to
00:22:36
figure out all the data from the emr
00:22:39
then i'm going to have a then i'm going
00:22:40
to have part rule-based approach and
00:22:42
then i'm going to have something else so
00:22:44
there are different hybrid forms of care
00:22:46
pathways that are going on but
00:22:47
eventually it's going to evolve into one
00:22:49
scare pathway
00:22:51
so in algorithm versus ai data
00:22:54
codes
00:22:55
not rule codes now one of the other
00:22:57
things that are truly about artificial
00:22:59
intelligence is that if there's a
00:23:01
continuous stream of data those rules uh
00:23:04
that are effect the word rule with may
00:23:06
still be
00:23:07
the the way the artificial intelligence
00:23:09
predicts can change because there is a
00:23:11
continuous stream of data and as the
00:23:12
data changes the artificial intelligence
00:23:14
algorithm itself can change which is
00:23:16
very different from rule based because
00:23:18
rule based gets stuck right this is some
00:23:20
expert came in or whatever thousands of
00:23:22
different uh scientific observations
00:23:24
that created that rule-based algorithm
00:23:26
but the benefit of artificial
00:23:28
intelligence is that it can continuously
00:23:30
learn and that is the key thing however
00:23:34
that is not currently present in
00:23:36
medicine
00:23:37
let me give you an example
00:23:39
this art this continuous learning is
00:23:41
what youtube does facebook does twitter
00:23:44
does there's a constant new trends and
00:23:46
then they shift their algorithm models
00:23:48
accordingly and then prevent from
00:23:50
whatever reason and you can introduce
00:23:52
some of different ways in which to
00:23:54
introduce some bias and that's one of
00:23:56
the ways that they work but what we want
00:23:58
to do is that we should in medicine have
00:24:00
the same opportunity but unfortunately
00:24:02
that's not true
00:24:04
to date i am not aware of any medical
00:24:07
algorithm that is constantly learning
00:24:10
they have learned from the data that
00:24:12
algorithm has static is based on the
00:24:15
data and is applied but it is not
00:24:18
it is not constantly learning and the
00:24:20
reason behind it is that there's a
00:24:22
significant amount of clinical data
00:24:24
shift and clinician data shift and there
00:24:26
is their major issues um
00:24:29
let me give you an example because that
00:24:31
would be easier
00:24:32
look we have a fantastic algorithm for
00:24:34
sepsis for predicting sepsis and as a
00:24:36
matter of fact it worked beautifully
00:24:38
before covert but after covert the
00:24:40
sepsis was completely different because
00:24:42
most pre-covert sepsis was bacterially
00:24:44
origin but post
00:24:46
covid we had viral sepsis and those
00:24:50
viral substances were very different for
00:24:52
the computer to predict those viral
00:24:54
differences it needs new forms of data
00:24:57
so that it can predict much better we
00:25:00
didn't do that because it's not
00:25:01
consistently learning environment we
00:25:03
don't have it because of multiple
00:25:04
reasons but for now that's the biggest
00:25:06
issue because we do not have a
00:25:07
continuous learning environment that
00:25:09
sepsis algorithm freaking broke as a
00:25:11
matter of fact the company has to
00:25:12
retract it so that's the key issue in
00:25:15
terms of medicine and which is the the
00:25:17
biggest issue is the data data data high
00:25:20
quality data that you can process in
00:25:22
real time to produce predictions in real
00:25:25
time that could potentially be shifting
00:25:27
because of environment country
00:25:29
genetic background etc etc so that's the
00:25:32
big issue why we do not have continuous
00:25:35
learning of data engineers learning
00:25:38
algorithm which is the true form of
00:25:39
machine learning in medicine
00:25:41
so that's what another vision and that's
00:25:43
one of the key issues that another issue
00:25:45
is i keep asking me will ai will yeah i
00:25:48
replace physicians absolutely not
00:25:50
now it's already the current state if
00:25:52
you look at it it is basically combining
00:25:54
human and ai in terms of
00:25:56
very narrow tasks so if you have a
00:25:58
narrow task cd brain hemorrhage or no
00:26:00
hemorrhage yes but when i look at us cat
00:26:03
scan i mean it's not just like if it's a
00:26:05
bleed or not i mean it's a 70 80 year
00:26:07
old female that has significant atrophy
00:26:09
why the hell so i can talk about
00:26:11
dementia possibly with the same patient
00:26:13
while i'm talking about enzyme or
00:26:14
talking about stroke where i see that
00:26:16
the patient has calcification in places
00:26:18
where there were no calcifications so in
00:26:20
a complete narrow task artificial
00:26:22
intelligence is right there
00:26:24
in computer vision and i mean i'm going
00:26:26
to talk about they're definitely places
00:26:28
where artificial intelligence
00:26:30
significantly lacks and there are places
00:26:32
where artificial intelligence is better
00:26:33
than physicians
00:26:35
however in future we're going to see
00:26:37
that since we're going to have more and
00:26:39
more high quality data added we will are
00:26:41
building data pipelines in which
00:26:43
artificial intel data high quality data
00:26:45
is going to be input for continuous
00:26:46
learning we're going to see that data
00:26:48
the the process shift in which in more
00:26:52
and more tasks artificial intelligence
00:26:54
is going to get better and better from
00:26:56
humans i mean it's quite a while away
00:26:58
about 20 to 30 years away in terms of
00:27:00
truly seeing that massive impact in
00:27:03
medicine there are definitely areas in
00:27:05
which artificial intelligence is going
00:27:07
to be the standard
00:27:09
on which physician overlay reading will
00:27:11
be there and this is where we come in
00:27:14
e.i will not replace physicians however
00:27:16
physicians that use artificial
00:27:18
intelligence will replace a physician
00:27:19
who do not
00:27:21
that's something literally if this one
00:27:23
slide that you have to take care of is
00:27:24
this you have to learn it exactly as you
00:27:27
have learned everything else and
00:27:28
secondly initially at least in the
00:27:30
majority of cases you're going to see
00:27:31
that artificial intelligence and
00:27:33
automation that is added is basically
00:27:36
allowing you to have better decision
00:27:39
support in terms of faster care no
00:27:41
latency of care improved care outcomes
00:27:43
etc etc but at the end of the day you
00:27:46
have the helms of the
00:27:49
control in terms of patient management
00:27:51
is concerned think of it as the horse or
00:27:52
ski lag
00:27:54
physician
00:27:55
but the horse is getting better the
00:27:56
whole time ever i mean it literally it's
00:27:59
going from a horse to our ferrari so in
00:28:01
terms of moving in
00:28:03
in terms of improving accuracy and
00:28:05
improving predictions
00:28:07
so
00:28:08
now we come to development of artificial
00:28:10
intelligence as i talked about data and
00:28:11
data data look the data is is massively
00:28:15
improving as a matter of fact there's
00:28:16
one third of clinical data in unite in
00:28:19
production so if there's
00:28:22
100 data points are created about 33 of
00:28:25
them are coming from the health care
00:28:26
industry health care
00:28:28
is a massive
00:28:30
reason for expanding data in the world
00:28:34
and we are expanding data significantly
00:28:36
i mean youtube videos you know etc etc
00:28:39
so we are having a lot of data that is
00:28:41
coming out but healthcare still creates
00:28:43
a lot of data
00:28:44
and unfortunately that is
00:28:45
multi-dimensional data what i mean by
00:28:47
that is that there's disease data you
00:28:49
know radiomics genomics biomics and then
00:28:52
there's health data which we are you
00:28:54
know creating with our smart watches and
00:28:56
ecg machines voice analysis etc etc and
00:28:59
then there's claims data you know with
00:29:00
healthcare industries coming up with it
00:29:02
sorry healthcare insurance is coming in
00:29:04
and then of course we have clinical
00:29:05
trials data
00:29:06
bio data and clinical registries data
00:29:08
and those can be pulled on
00:29:10
which is important first of all there's
00:29:12
a lot of volume but unfortunately the
00:29:15
biggest curse in in
00:29:17
medicine is that it deman it's
00:29:19
dimensional over time as well so my body
00:29:21
metabolics is very different when i was
00:29:23
20 years old i mean i'm becoming an old
00:29:25
man now so clearly the metabolics have
00:29:27
changed so over time the data
00:29:29
characteristics change so that's where
00:29:31
the curse of dimensionality in
00:29:32
healthcare comes in
00:29:34
and then more importantly more and more
00:29:36
convergence is happening right i mean as
00:29:37
i said we have more digital therapeutics
00:29:40
are coming up which is going to create
00:29:41
new forms of data we're going to have
00:29:43
new forms of genetic analysis which
00:29:45
creates more data then we have new
00:29:47
manufacturing done so there was more
00:29:48
data so we're going to have more and
00:29:50
more data coming in into the pipeline
00:29:52
that no matter what you do you're going
00:29:54
to need artificial intelligence to churn
00:29:56
it period there's no other way around it
00:29:58
because it's just impossible for anyone
00:30:00
to take that into a data science and
00:30:02
then make sense of it because you have
00:30:04
to have that that data to be done and
00:30:07
that's where another thing comes in you
00:30:09
do have to have a purpose right you just
00:30:11
don't collect data for no reason you
00:30:13
collect data to process it that's key
00:30:16
thing that is why the development of
00:30:18
artificial intelligence is going to
00:30:19
happen in stacks so i don't know if you
00:30:22
have any brother or sister in in
00:30:24
software services but you should talk to
00:30:26
them they talk about software as a
00:30:28
service sas that's the key term software
00:30:30
as a services basically you find a small
00:30:32
problem and you develop an automation
00:30:34
solution on it and then you apply it to
00:30:36
the big stack and then you another
00:30:37
problem big stack another problem is
00:30:39
stack it's exactly the same artificial
00:30:41
intelligence is developing medicine i
00:30:43
have a problem in terms of artificial in
00:30:45
stroke i have medical imaging analysis
00:30:48
through computer vision one stack i need
00:30:50
natural language processing to process
00:30:52
patient reported data or also clinical
00:30:55
reports i have another uh
00:30:58
transfer and management issue that do
00:30:59
that i have another stack of genomics
00:31:02
all these stacks are currently going to
00:31:03
be in development on different stacks
00:31:06
and these are software services
00:31:08
eventually these software and services
00:31:09
is going to become immature and then
00:31:11
when they're going to become mature
00:31:12
they're going to combine and when
00:31:14
they're going to converge that's going
00:31:15
to turn into a full artificial
00:31:17
intelligence that is going to be more
00:31:19
more usable and believe me uh this is
00:31:21
disruptive and can happen suddenly like
00:31:23
the kodak moment right i mean nobody's
00:31:25
using plain films anymore everyone's
00:31:27
doing digital photography and i have
00:31:29
another lecture on the convergence of
00:31:30
virtual care and artificial intelligence
00:31:32
because the virtual care is now becoming
00:31:34
a very important part of producing
00:31:36
ground truth valuable confirmed
00:31:39
validated data in clinical so therefore
00:31:42
we're you know if you're interested in
00:31:43
looking at convergence more you can look
00:31:45
at that lecture
00:31:46
now
00:31:48
there are three different pillars of
00:31:49
artificial intelligence one of them is
00:31:51
computer vision which goes about digital
00:31:53
pathology etc then predictive analytics
00:31:56
and natural language processing
00:31:58
so let's go into that so computer vision
00:32:00
is all about neuroideology in your terms
00:32:02
and neuropathology and then in terms of
00:32:04
video analysis if the patient is walking
00:32:05
you can see if the patient has stranded
00:32:08
walk is it the spine issue is it the
00:32:10
multiple sclerosis patient in which we
00:32:12
are using image and video analysis to
00:32:15
quantify that walk and how the other
00:32:17
medic the new medication you're going to
00:32:19
develop is going to improve so we're not
00:32:21
going to talk about virtual clinical
00:32:22
trials right now but the idea is that
00:32:24
image and video analysis computer vision
00:32:26
then we have predictive analytics and
00:32:28
this is where the decision support and
00:32:30
trend analysis comes in simple pulse
00:32:32
oximetry is coming through the icu you
00:32:33
can say that if the pulse ox is changing
00:32:35
is the patient developing high increase
00:32:37
intracranial pressure or as the patient
00:32:39
has different heart rates and
00:32:41
blood pressure monitoring are changing
00:32:43
can you predict at least sometimes it as
00:32:44
a matter of fact their studies have
00:32:46
predicted a sudden uh development of
00:32:48
cardiac arrest in eyes sick sepsis
00:32:51
patients up to 15 minutes earlier so
00:32:54
that's where the prediction trend
00:32:55
analysis come in in the predictive
00:32:56
analytics analytics portion and then
00:32:59
finally natural language processing
00:33:01
natural language processing is the most
00:33:02
exciting and the most important feature
00:33:05
as a matter of fact um
00:33:07
that's why google shines a lot in terms
00:33:10
of you know its prowess in artificial
00:33:12
intelligence because that their whole
00:33:14
thing was behind search is natural
00:33:17
language processing all that word data
00:33:19
on the internet and how to collect
00:33:21
accumulate and create wisdom from it
00:33:24
there are two areas in it one is speech
00:33:25
and the other one is text speeches text
00:33:27
to speech speech to text all of them use
00:33:29
artificial intelligence and as far as
00:33:31
text is concerned classification of that
00:33:34
extraction of that summarization of that
00:33:35
translation of that as a matter of fact
00:33:37
ibm watson was
00:33:39
summarizing automatically
00:33:41
some of the cancer trials and while the
00:33:43
patient is being seen and when you
00:33:45
search on that they would recommend uh
00:33:47
which clinical trial is applicable to
00:33:49
that particular patient and pull up a
00:33:51
summary of that as well so clearly there
00:33:53
are different areas where there are
00:33:55
applications but this is a sort of a
00:33:57
chart for you to understand
00:33:58
what different areas of artificial
00:34:00
intelligence are and what they are
00:34:01
concentrating on
00:34:03
and we talked about data and convergence
00:34:06
data data data it is all about data
00:34:08
and uh data is literally the new oil
00:34:11
nobody talks about enron as the most
00:34:13
valuable company we talk about google
00:34:14
facebook tesla amazon and the reason
00:34:17
that tesla is the most valuable company
00:34:18
is because it doesn't have a freaking
00:34:20
four wheels that is better than any
00:34:21
other car because it is not a car
00:34:23
company it is a data company
00:34:25
and
00:34:26
one of the key things that you need to
00:34:28
keep working on and this is one of the
00:34:30
most important papers from last year is
00:34:32
that everyone wants to do the sexy model
00:34:34
work in artificial intelligence and i
00:34:35
want to create models and then turn
00:34:37
algorithms and everything
00:34:39
data work is the most important work if
00:34:41
you have a beautiful data like data
00:34:43
pipeline you will have
00:34:46
golden area to develop artificial
00:34:48
intelligence to develop ground truth and
00:34:50
once you have that you can be amazing in
00:34:52
terms of artificial so data work data
00:34:54
work data work it is not just modeling
00:34:57
and all that to be honest it's getting
00:34:59
very easy one of the other things you
00:35:01
have to remember whenever you provide
00:35:03
automation to anything automation is
00:35:05
like a catalyst if you have a process
00:35:08
that is garbage it's gonna catalyze
00:35:10
garbage into more garbage and if you
00:35:12
have an area in which you are efficient
00:35:15
and you put in a catalyst then that
00:35:17
efficiency is going to be expanded as
00:35:18
well so it's very important that
00:35:21
wherever artificial intelligence is
00:35:22
applied it is applied in a process that
00:35:26
has been optimized and efficient before
00:35:28
you apply artificial intelligence
00:35:30
because if you don't then that ai is
00:35:32
going to be a bigger problem in terms of
00:35:35
where it is because it basically is a
00:35:37
tool it is a catalyst anytime you have
00:35:39
that sort of automation make sure that
00:35:41
you have that
00:35:42
silk smooth out before you make
00:35:44
applications
00:35:47
now let's get to artificial intelligence
00:35:49
and neurology we're going to talk
00:35:50
quickly about two major things one is
00:35:53
stroke you see stroke is 861 billion
00:35:56
problem huge issue there's increasing
00:35:58
incidence increasing debts people are
00:36:00
living with it more more and more as a
00:36:02
matter of fact if you literally do one
00:36:03
minute delay you are basically cleaning
00:36:06
1.9 million neurons and 14 billion
00:36:08
synapses and if there's a fast
00:36:10
progressive stroke you can use a limb
00:36:13
lose a limb completely in five minutes
00:36:16
we definitely need to reduce time right
00:36:18
if you're not going to reduce time
00:36:20
we're going to have a big issue and
00:36:22
that's where artificial intelligence
00:36:23
comes in and this is one of the earliest
00:36:26
application of artificial intelligence
00:36:27
that is commercially available
00:36:30
and
00:36:30
viable
00:36:31
there's a difference commercially
00:36:33
available clearly this is fda approved
00:36:34
people have seen it people have applied
00:36:36
it i use this software every day in my
00:36:38
life and i'm providing stroke care and
00:36:41
it is getting reimbursed so people are
00:36:43
getting paid for the hard work and
00:36:45
artificial intelligence they have done
00:36:46
because it's just not about academics
00:36:48
it's about producing
00:36:50
a product that is commercially viable
00:36:52
and reimbursable
00:36:54
so
00:36:54
there are multiple software packages
00:36:57
this is one visa i don't know if you're
00:36:58
aware of dr adnan siddiqui and amir
00:37:00
hussain they are fantastic people here
00:37:02
in us amir is from egypt and atlanta
00:37:04
she's from pakistan who has done this
00:37:06
study and showed that artificial in
00:37:08
intelligence decreases latency to care
00:37:10
by 66 minutes think about the number of
00:37:13
limbs
00:37:14
you can save in lives you can save with
00:37:16
that because you can get to the patient
00:37:18
faster and earlier if you don't have a
00:37:20
thermectomy center it's to
00:37:22
apply it right you have an efficient
00:37:24
system
00:37:25
to get efficient results you just don't
00:37:27
put artificial intelligence where you
00:37:29
can't create a difference so remember
00:37:31
that but at the end of the day that
00:37:33
these are the value that artificial
00:37:34
intelligence is bringing we can see it
00:37:36
in interstable hemorrhage dissection we
00:37:38
can automate it into stable hemorrhagic
00:37:40
detection as we want we can have
00:37:42
significant amount of commercial
00:37:43
software that are available this is the
00:37:45
list
00:37:46
that is there and you have to find the
00:37:48
right problem to find the right solution
00:37:51
this is a
00:37:53
complete video that i did
00:37:54
make sure you look at it in terms of
00:37:56
role of artificial intelligence and
00:37:57
telestroke and how it
00:38:00
becomes more than life in terms of
00:38:02
decreasing the latency of care itself
00:38:06
and this is the paper that got published
00:38:08
about the same thing
00:38:09
in terms of role of artificial
00:38:11
intelligence and stroke and telestroke
00:38:14
the second big reason is biomarkers i
00:38:16
told you about it because one of the key
00:38:18
issues in
00:38:19
prediction of neurological disorders is
00:38:22
to have something that i can have to to
00:38:25
diagnose the patient and more
00:38:27
importantly not just the diagnosis early
00:38:29
diagnosis so i can make a change to
00:38:31
predict how the patient is going to move
00:38:33
and then any intervention early
00:38:35
intervention that can be done to correct
00:38:38
that bad pathological pathway that is
00:38:40
moving forward it is most important in
00:38:43
terms of stroke and in terms of
00:38:47
alzheimer's and everything and we're
00:38:48
working on it right it's not like we're
00:38:50
not stopped working on blood
00:38:53
based biomarkers we're looking at
00:38:54
hemorrhaging we're looking at
00:38:56
subarachnoid hemorrhage we're looking at
00:38:57
csf analysis in some of these patients
00:38:59
to find
00:39:01
lab-based biomarkers blood-based
00:39:03
serum-based biomarkers for these
00:39:05
patients however it is becoming harder
00:39:07
and harder and we have to produce
00:39:09
different methods of doing it and one of
00:39:11
the method is
00:39:13
using deep learning artificial
00:39:15
interlearning in a multimodal way to
00:39:18
predict
00:39:19
early if there is anything going on and
00:39:21
one of the best applications this has
00:39:23
been in alzheimer's in which what we're
00:39:25
seeing is that we can combine imaging
00:39:27
genetics and clinical test data this is
00:39:30
not available commercially this is
00:39:32
currently in research only however what
00:39:36
we are seeing the trend going forward is
00:39:38
that when we have combination of these
00:39:41
different data sources i can predict
00:39:44
alzheimer's at the age of 55 what am i
00:39:46
going to do with it well currently
00:39:47
nothing i mean there's only one drug
00:39:49
that is available which is highly
00:39:50
controversial but at the end of the day
00:39:52
if i have to develop a drug i cannot
00:39:54
give it to a person who already has
00:39:56
advanced dementia what am i going to do
00:39:57
the brain is already
00:39:59
problematic we need to early intervene
00:40:01
so the minute i'm able to have
00:40:03
authenticated clinically validated
00:40:06
digital or multi-modal biomarker i can
00:40:09
intervene with a new medication study
00:40:11
that medication and see how it goes so
00:40:14
we're going to see that more and more as
00:40:16
far as biomarkers are concerned
00:40:18
now i have all of this and more in terms
00:40:21
of my courses that are coming
00:40:23
as far as how to choose a medical
00:40:25
specialty in this new post
00:40:27
pandemic digital healthcare era in which
00:40:29
i'm going to talk about artificial
00:40:30
intelligence virtual care and advanced
00:40:32
practice providers and i'm going to be
00:40:35
giving out a total of 100 participants
00:40:37
significant discount in honor of dr
00:40:40
wasse so i'm going to send it because he
00:40:42
is a beacon of neurology in pakistan and
00:40:45
i have been impressed that how he has
00:40:48
managed to do all this all the time so
00:40:50
i'm going to send it to him and he can
00:40:52
distribute the codes to anyone and
00:40:54
people can take this other course in
00:40:57
much more detail as far as artificial
00:40:59
intelligence virtual care and other
00:41:01
things like lifestyle considerations
00:41:02
financial considerations etc so it's
00:41:05
being built it's going to be available
00:41:06
next month and i hope to see you there
00:41:08
as well
00:41:09
another thing that you might want to
00:41:10
help me with is about
00:41:12
neurologypocketbook.com
00:41:14
you can go there multiple chapters that
00:41:15
i'm adding about digital neurology in
00:41:17
terms of what is digital health what is
00:41:19
m health
00:41:21
mobile health in neurology and then
00:41:23
there are chapters on digital education
00:41:25
in neurology artificial intelligence
00:41:27
neurology like staple ischemia and this
00:41:28
is all open access so you can copy paste
00:41:31
it put it in your presentations etc and
00:41:33
maybe contribute if you want to
00:41:35
this is the website and then of course
00:41:37
everything that i do is on one link tree
00:41:39
including twitter etc all that thank you
00:41:43
so much for your help and i'm very very
00:41:45
grateful for your time if you save a
00:41:46
life it is as if you saved the life of
00:41:48
mankind please make sure you like
00:41:50
comment share and subscribe to my
00:41:52
youtube channel and my newsletter if you
00:41:55
want to get in touch with me the best
00:41:56
way is to go through twitter or while
00:41:58
linkedin also make sure you follow the
00:42:00
academy website for regular updates
00:42:02
thank you so much

Etiquetas

Artificial Intelligence
Neurology
Stroke
Epilepsy
Healthcare
Machine Learning
Deep Learning
Biomarkers
Patient Care
Medical Technology