The future of artificial intelligence in radiology: Prof. Dr. med. Mathias Goyen
Summary
TLDRThe speaker, a radiologist and professor, discusses the transformative impact of artificial intelligence (AI) in radiology. With the exponential growth of medical data, AI is essential for managing information overload and reducing medical errors. The talk highlights AI's role in personalized medicine, improving diagnostic accuracy, and enhancing patient care. The speaker advocates for embracing AI as a tool to augment radiologists' capabilities, not replace them, emphasizing the importance of a technology quotient (TQ) for future professionals.
Takeaways
- 📈 Rapid Data Growth: The healthcare industry is generating an enormous amount of data, doubling in a mere 73 days, highlighting the exponential growth of medical information.
- 📚 Overwhelming Information: With 5,600 medical journals producing 800,000 articles annually, the volume of medical literature is vast, and radiologists face significant challenges in keeping up with it.
- 🔍 Radiologist's Challenge: Radiologists are tasked with analyzing tens of thousands of images in a single shift, a task that has become increasingly demanding with the rise in data volume.
- 🚑 Medical Errors: Medical errors are alarmingly common, with an estimated 350,000 patients dying annually in Europe due to hospital-related errors, emphasizing the need for improved accuracy in diagnoses.
- 👩⚕️ Healthcare Worker Shortage: The global shortage of healthcare workers is a pressing issue, with a projected 14 million missing workers by 2035, largely due to retirements and a lack of new professionals entering the field.
- 🤖 AI in Healthcare: Artificial intelligence (AI) is being integrated into healthcare to assist with the management of data and to improve diagnostic accuracy and efficiency.
- 🛠️ AI Implementation Levels: AI is being implemented at various levels in healthcare, including individual imaging devices, departmental workflow optimization, and enterprise-level patient flow management.
- 👥 Personalized Medicine: The shift towards personalized medicine, based on individual genetic differences, is transforming healthcare from a 'one size fits all' approach to tailored treatments and diagnostics.
- 🔑 Unlocking Potential: AI's potential to highlight critical cases, such as pneumothorax, can significantly improve patient outcomes by ensuring timely diagnoses and prioritizing urgent cases.
- 🤝 Need for Partnerships: GE Healthcare recognizes the importance of partnerships for developing AI applications and ensuring their clinical relevance and utility.
- 🚀 Future Opportunities: The rise of digitization will lead to the disappearance of certain jobs while creating new ones, emphasizing the importance of adaptability and a high 'Technology Quotient' (TQ) for future success.
Q & A
What is the role of the speaker in the context of the script?
-The speaker is the Chief Medical Officer for GE Healthcare in Europe, a radiologist, and a professor at Hamburg University, responsible for overseeing medical affairs, medical education, and healthcare in Europe.
How rapidly is medical data doubling according to the speaker's presentation?
-The speaker mentions that in 2010, medical data doubled every 3.5 years, but by 2020, it was doubling in just 0.2 years, which equates to approximately every 73 days.
What is the significance of the number of articles published in medical journals in relation to the data explosion?
-The speaker points out that there are 5,600 medical journals publishing 800,000 articles annually, emphasizing the sheer volume of new information being generated in the medical field.
What is the comparison made by the speaker between the amount of information in a mammogram and a New York telephone book?
-The speaker illustrates the vast amount of data in medical imaging by stating that there is more information in a mammogram than in the telephone book of New York.
How has the workload of a radiologist changed over the past 15 years according to the script?
-The speaker notes that a radiologist in a 12-hour shift now looks at 50,000 images, compared to only 500 images 15 years ago, indicating a significant increase in data to be analyzed.
What is the estimated number of patients dying annually in Europe due to medical errors, as mentioned in the script?
-The speaker estimates that 350,000 patients die every year in Europe due to medical errors, comparing this number to the population of a city like Venice or Toulouse.
What is the current global shortage of healthcare workers, and what is the projected shortage by 2035?
-The speaker states that the current global shortage of healthcare workers is seven million, with an estimated shortage of fourteen million by 2035.
What is the speaker's view on the role of disruptive technology in healthcare?
-The speaker believes that the best disruption is an innovation that works in the background without being obtrusive, rather than technology that disrupts the workflow of healthcare professionals.
What does the speaker mean by 'personalized medicine' and how does it differ from traditional evidence-based medicine?
-Personalized medicine refers to an approach that tailors diagnosis and treatment to the individual's unique genetic makeup, as opposed to traditional evidence-based medicine, which often follows a one-size-fits-all approach.
Can you explain the example of personalized medicine provided in the script involving Herceptin-positive breast cancer?
-The speaker uses the example of Herceptin-positive breast cancer, where a specific monoclonal antibody called trastuzumab (Herceptin) can extend life but has side effects. Personalized medicine ensures that only patients who are Herceptin positive receive the drug, avoiding unnecessary side effects for others.
What are the three levels of AI application in healthcare as described by the speaker?
-The speaker identifies three levels of AI application in healthcare: individual level (integrating AI into medical scanners), departmental level (using AI to streamline workflows in radiology departments), and enterprise level (using AI for patient flow management in hospitals or hospital networks).
What is the purpose of the 'Command Center' in a hospital as mentioned in the script?
-The 'Command Center' in a hospital uses predictive analytics to manage patient flow and experience, providing real-time data on bed availability and other resources to optimize hospital operations.
Why is the speaker emphasizing the importance of partnerships in developing AI applications for healthcare?
-The speaker emphasizes partnerships because they believe that the majority of smart people and innovative ideas are outside of GE. Partnerships are crucial for developing clinically useful applications and for gaining insights into the practical application and user experience of these technologies.
What does the speaker suggest is the key to thriving and surviving in the future job market, particularly in relation to AI and digitization?
-The speaker introduces the concept of 'Technology Quotient' (TQ), which measures one's openness to embracing new technologies. A high TQ indicates an ability to adapt to digitization and is key to thriving in the future job market.
Will AI replace doctors and radiologists according to the speaker's perspective?
-The speaker believes that AI will not replace doctors and radiologists but will instead augment their capabilities, similar to how autopilot in airplanes has not replaced human pilots but has enhanced their capabilities.
What is the speaker's final suggestion regarding the adoption of AI in healthcare?
-The speaker suggests that healthcare professionals should responsibly embrace AI instead of fearing it, as it offers tremendous opportunities to improve diagnostics, speed, and accuracy, and to humanize radiology.
Outlines
🧠 The Future of AI in Radiology
The speaker, a chief medical officer for GE Healthcare in Europe and a radiologist, introduces the rapid growth of data in healthcare, highlighting the acceleration from medical data doubling every 73 days to the immense number of articles published in medical journals. The speaker emphasizes the challenges faced by healthcare professionals in managing this data, such as the increase in medical images and the high rate of medical errors leading to patient deaths. The shortage of healthcare workers is also addressed, with a predicted global shortage of 14 million by 2035. The speaker advocates for a shift towards non-disruptive, innovative technology that can work in the background to assist healthcare professionals.
🛠️ Personalized Medicine and the Role of AI
This paragraph delves into the transition from generalized to personalized medicine, using the example of Herceptin-positive breast cancers and the importance of molecular analysis to ensure the right treatment is given to the right patients. The speaker discusses the three main areas of personalized medicine: diagnostics, therapeutics, and monitoring, and the integration of various data sources, including omics data and wearables. The exponential growth of data is illustrated with an analogy of walking around the globe, emphasizing the need for AI to manage this data efficiently.
🚀 AI Integration in Healthcare at Multiple Levels
The speaker explains how AI is being integrated into healthcare at the individual, departmental, and enterprise levels. At the individual level, AI is implemented in medical scanners to assist in identifying critical conditions like pneumothorax, highlighting the importance of timely diagnosis. The departmental level focuses on operational AI to streamline workflows in radiology departments, and the enterprise level involves using AI for patient flow management across hospitals or networks. The speaker also touches on the potential of AI in predictive maintenance, as exemplified by the application in aviation.
🏥 Command Centers for Patient Flow Management
The speaker describes the concept of command centers within hospitals, which utilize predictive analytics to manage patient flow and experience, particularly in emergency departments and ICUs. An example from Bradford, UK, is given where a command center was implemented to improve bed allocation and patient management. The command center provides transparency and optimizes the use of available beds, potentially increasing the number of 'virtual beds' through better management.
🤝 The Importance of Partnerships in AI Development
The speaker stresses the need for partnerships in developing AI applications, as the majority of smart people and potential users are outside of GE. Partnerships are sought worldwide to develop applications and to ensure their clinical usefulness. The speaker also introduces the concept of TQ, or technology quotient, which measures one's openness to embracing new technologies, and suggests that this will be crucial for future job prospects in the face of digitization and the emergence of new job roles.
🌐 AI's Impact on the Future of Radiology Jobs
The speaker discusses the potential impact of AI on the future of radiology and other jobs, emphasizing that while AI will not replace radiologists, it will change the nature of their work. AI is seen as a tool to assist with repetitive and time-consuming tasks, allowing radiologists to focus on more complex cases and patient interaction. The analogy of autopilot in airplanes is used to illustrate that AI will augment rather than replace human professionals. The speaker concludes by encouraging the audience to embrace AI responsibly and not to fear it.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Radiology
💡Healthcare Data
💡Medical Errors
💡Healthcare Workforce Shortage
💡Personalized Medicine
💡Wearables
💡Exponential Growth
💡Digital Twin
💡Command Center
💡Technology Quotient (TQ)
Highlights
The rapid doubling of medical data, with a doubling time of only 0.2 years in 2020, necessitates innovative approaches to manage the information overload.
The sheer volume of medical journals and articles, along with the complexity of data such as in mammograms, underscores the challenge of data management in healthcare.
The significant number of medical errors, with up to 350,000 patients dying annually in Europe due to hospital errors, highlights the urgent need for improved diagnostic methods.
A shortage of healthcare workers, with a current global deficit of seven million, is a pressing issue that is expected to worsen by 2035.
The importance of non-disruptive innovation in healthcare, where technology should support rather than interfere with medical professionals' work.
The concept of personalized medicine, moving away from a one-size-fits-all approach to a more tailored and individualized healthcare solution.
The use of monoclonal antibodies like trastuzumab (Herceptin) in treating aggressive breast cancers, illustrating the application of personalized medicine.
The exponential growth of data, likened to walking 26 times around the globe in 30 steps, emphasizing the scale of the data challenge in healthcare.
The integration of AI into imaging analytics at the individual, departmental, and enterprise levels to streamline workflows and improve patient care.
AI's role in highlighting critical cases, such as pneumothorax, on mobile X-ray systems to expedite diagnosis and treatment.
Optimizing imaging protocols and scheduling systems with AI to reduce patient wait times for MR scans, showcasing AI's practical application in private practices.
The implementation of command centers in hospitals, using AI for predictive analytics to manage patient flow and improve emergency department efficiency.
The value of partnerships in developing AI applications, emphasizing the need for collaboration to ensure clinical relevance and utility.
The introduction of TQ (Technology Quotient) as a measure of one's openness to embrace new technologies in the face of digitization.
AI's potential to replace repetitive and mundane tasks in radiology, freeing up radiologists to focus on more complex cases and patient interaction.
The analogy of AI to an autopilot in aviation, augmenting rather than replacing the roles of pilots and radiologists, emphasizing the collaborative potential of AI.
A call to responsibly embrace AI in radiology, viewing it as an opportunity to enhance diagnostic capabilities and humanize patient care.
Transcripts
[Music]
good morning ladies and gentlemen as
said and the chief medical officer for
GE healthcare in Europe I'm a
radiologist and I keep my professorship
at Hamburg University and so I regularly
teach to try to stay up-to-date MA but
my responsibility is overseeing medical
medical affairs medical education
everything that has to do with medical
4G healthcare in Europe today it's about
the future of artificial intelligence in
radiology and let me start by just
giving you a couple of numbers the
amount of data that is being generated
in healthcare is simply mind-blowing in
2010
it took three and a half years for
medical data to double only ten years
later in 2020 this year it's only 0.2
years this is 73 days if you think about
it this is the time from now till Easter
so the medical data is doubled that's
really incredible
there are 5,600 medical journals putting
out 800,000 articles a year there is
more information in a mammogram then
there isn't the telephone book of New
York if they're still there still is a
physical telephone book and if you think
about it a radiologist in a 12-hour
shift is looking at 50,000 images only
15 years ago these were like five
hundred images so this is really a lot
of data and health care professionals
clinicians radiologists radiographers
have to deal with on the other hand
there are a lot of medical errors
happening every year 40 million of
course not every medical error is fatal
but it is estimated that if you if we
take the numbers for Europe up to
350,000 patients die every year due to
medical errors that happened in the
hospital to make this a little bit more
tangible
350,000 and people that's that's a city
like Venice in Italy or Toulouse in in
France gone every year so this is really
a big deal on the other hand we have a
shortage of healthcare workers again two
numbers this year the global shortage is
seven million and if we think in 2035 it
is estimated there are fourteen million
people and missing you know legging in
healthcare and this is due to the fact
that a lot of staff is retiring and not
enough young people you know moving into
the profession or leaving for better
paid jobs in the industry so this is
another big challenge so I don't want to
demotivate you but this is like you know
the ramifications this is what we are
dealing with right and so and I always
hear you know we need this and
disruptive technology now and and and
you know you know what a surgeon needs
least is it is it is a technology this
which disrupts him from his surgery
right I mean the best disruption is is a
is an innovation that is non-disruptive
that is unfolding it's magic in the
background and and not really and you
know that it's not not a parent it's
it's just inconspicious lee working in
the background so having said that we
really have to start doing things
differently and we also have to stop
doing things quite frankly we also have
to stop doing things we used to do and I
don't know if you know that you share
99% 99.5% of your DNA with a person
sitting next to you just look at your
neighbor probably hard to believe 99.5%
of your DNA is completely identical so
that means we differ in only 0.5% of our
DNA that's not much right but on the
other hand if you do the math this
translates into three
million base-pairs and i don't think you
need to be a professor of genetics to
understand that a drug that i use to
lower my cholesterol or my blood
pressure my high blood pressure has a
potential different effect in me than in
you in you and in you and why is that
because we are so different
these 0.5% make the difference this
understanding is very important and
paved the way from evidence-based
medicine toughts personalized medicine
we use to diagnose all the patients
pretty much in the same way and then the
therapy was the same now we are going to
a more individualized approach for
diagnosing patients and also we have
tailored therapies let me just give you
one simple example of where we are
applying personalized medicine for many
years in clinical routine this is a you
know the topic is breast cancer you know
that probably 15 to 20 percent of all
breast cancers are so called Herceptin
positive breast cancers the problem with
those kind of breast cancers is they are
very aggressive and the prognosis is is
rather poor on the other hand there is
some light at the end of the tunnel
because there is a monoclonal antibody
it's called trastuzumab Herceptin and
which really helps those patients to
extend their life the problem is or the
challenge that this drug comes with some
side effects including cardiac toxicity
so you want to be sure that you only
give trusted sumup to those patients
that are Herceptin too positive because
otherwise if you give it to every
patient with newly diagnosed breast
cancer those patients only get the side
effects and there is no effect so that
means that in every patient with breast
cancer a so-called molecular
and analysis is done to really look if
this patient is Herceptin positive and
of course only in those patients
Herceptin is given very easy example of
personalized medicine so in personalized
medicine we are moving away you know
from this generalized approach one size
fits all more to a tailored
individualized approach in healthcare or
you could say we are moving you know
from a philosophy where you know every
patient is diagnosed the same way to
really a tailored therapy not for each
individual patient but probably for some
cohorts of patients and if you look at
personalized medicine
there are basically three buckets there
is the diagnostic bucket there is the
therapeutic bucket and then there is the
monitoring part we are dealing with
traditional radiology data in vivo data
and then we have all these kinds of
omics data in vitro data coming from lab
from pathology from your wearables from
the EMR just one remark regarding
wearables I don't know who has a
wearable or has a Fitbit or something
people who have have a wearable usually
don't need it because they are athletic
anyway I mean the wearable was invented
as an option for athletes and now we
have to translate or transform this into
a medical device I mean the 80 year old
patient with the BMI of 35 sitting on
the couch the entire day eating
chocolate this patient probably is in
need is in need of a wearable or
probably it's too late and in this kind
of patient but it's it's interesting
what is happening with this variable
market and when we are talking about the
explosion of data and you've heard about
it we are talking about exponential
growth compared to linear growth and let
me just give you a quick example
that I think nicely illustrates what
exponential growth really means just
assume I have a step length of one meter
to make it easier so in a linear in
linear growth if I walk 30 steps I have
walked 30 meters if my step length is 1
meter so in an exponential growth if my
starting step length is 1 meter I have
walked 26 times around the globe after
30 steps it's really incredible it's
mind-blowing and just I want you to keep
this in mind and when someone talks
about you know exponential growth
sometimes they show these graphs they go
up and you think wow this is really they
really go up but I mean 30 excursions 26
times around the globe so how can we
deal with this avalanche of data the
poor radiographer the poor radiologists
dealing with all these kinds of data
so now AI artificial intelligence is
coming into the game and before I talk a
little bit about AI let me just ask this
question and probably it's a little it's
little frightening
will a I become humans last an
intervention last invention because you
know from that time on everything that
is going to be invented will be Co
invented by AI probably if you think
about it and as you know we are
surrounded by AI in our daily lives who
is using AI we are all using AI at least
everyone who has a smartphone I guess
that's the vast majority of the people
here uses AI every day just a couple of
example every time we do a google search
and click on one of the suggested links
we are part of machine learning and
Google takes our click as an indication
that you know the results proposed were
pretty good otherwise who wouldn't have
clicked on them and is using and you
know all this feedback to
make the search and the search results
better
other examples include Netflix for
example every Friday I get an email what
to watch based on what I have watched
there are other examples if you use Siri
uber and also an example from GE health
care from our aviation colleagues we are
using artificial intelligence for
predictive maintenance in jet engines
the airline's really love that for every
engine that is actually sitting in a
plane there is a digital twin a
so-called digital twin on our computer
systems and as you can imagine a jet
engine generates a lot of data in real
time and this is sent to our computer
systems and then we can really go away
from this maintenance after a thousand
hours or 2,000 hours of operation more
towards a flexible maintenance approach
and of course there are lots of cost
savings that can be generated and it
makes complete sense
and if our computers indicate it makes
sense to do some maintenance tonight the
airlines can avoid technical issues
technical failures and with the need to
rebook patients and and cancel flights
and stuff like that
and we have integrated this approach
into health care so now let's take a
look when we talk about artificial
intelligence and imaging analytics in
healthcare where can we apply an AI I
see three different levels there is the
individual level and what I mean by that
is that we are implementing AI
capabilities right into our scanners in
to our CT systems into our M our scanner
into our ultrasound scanner then there
is the departmental level this is
operational AI we use AI to streamline
workflows in radiology departments in
private practices and then we have the
so-called
enterprise
level and enterprise level means we can
use a I and to look at patient flow in
entire hospitals or even hospital
networks I will come to that later let's
start with the individual level as I
said we can implement AI right into our
machines and I would like to give you an
example from x-ray you know that a
condition hospital sphere is especially
on the ICU is a pneumothorax a collapsed
lung and you also know if not diagnosed
correctly and in time it can be
potentially deadly and if you think
about the situation it's 3 o'clock in
the morning and the technician is
performing an x-ray with a mobile x-ray
system on the ICU the radiologist is
probably in the emergency room or is
reviewing some CT cases so the tech is
doing the images the chest x-ray and no
one is looking at those images and
research has shown it takes up to eight
hours till a radiologist actually looks
at at this x-ray and what we have now
implemented on on a mobile x-ray system
is implemented AI capabilities so the
technician is doing the x-ray on the ICU
and the implemented AI in an alert
system with a traffic light you know
green yellow red is is really
highlighting critical cases so that
means the tech can see oh it's very
likely that this patient has a collapsed
lung and then can send these images to
the pec system with high priority so
that the radiologist can directly look
at those images and what I like about
this example and it's not the case
whether the AI outperforms the
radiologist or the radiologist is still
better than the AI
it's just a hybrid model you know the
radiologists and the AI are working
together and the AI is is just
highlighting potential critical cases
this is a very nice example we have
introduced the system over a year ago
and this resonates very well and with
with our clinician because if you ask
them and diagnosing a pneumothorax and
it is is is really continues to be a
clinical pain point I mean if you look
at the image it's not that difficult to
diagnose a pneumothorax I mean there
there are there are tricky cases where
there are several pneumothorax but it's
just about highlighting out of those 10
images look at these two first because
it's very likely those patients have a
pneumothorax so you know the Prince
Prince of Wales and if you look at that
image well is the Prince of Wales really
showing the finger to the reporters
probably not you know as a radiologist
you always need the second the lateral
view and this was just outside
Kensington Hospital and you know when
and his wife gave birth to their third
child and he was just illustrating to
the reporters you know now I have three
three kids at home why do I show this
the best radiologist will miss the
diagnosis or will do the wrong diagnosis
if wrong images are highlighted so that
means we really have to pay attention
that the algorithm is validated and is
capable of of really highlighting the
critical images and not some images you
know they look fine and in the end it's
the radiologist who is signing you know
with you know by signing the report
saying that I have really reviewed the
images but you know it nowadays you can
generate a thousand images in ten
seconds and probably a radiologist
cannot cannot review all the
thousand images so we are having AI to
highlight critical cases so this is very
important and and that that we know or
that we really have to take care that
algorithms that we are developing with
our partners are really capable of
really highlighting the the critical an
image series so the second part is the
departmental level and as I said we can
use a I to make workflows better in
private practices in in hospitals in
radiology departments and this is just
one example from a private practice in
Germany in the frankfurt area there is a
customer of us and he owns nine or ten
imaging centers in the frankfurt area
and the waiting time to get an mr for
his patient was too long at least he
thought it was too long it was six weeks
you know if I give this presentation in
the UK they would love it it's only six
weeks waiting time for him this was
unacceptable so the first thing we did
we optimized the imaging protocols and
this is very important without
sacrificing the image quality so we were
able to reduce ten times by 16 percent
and keep keep the good image quality and
then you know together with dr. Alice
that's our customer we looked at you
know the scheduling system and we looked
at the radiology at the risk system and
we could actually you know optimize
processes here so in the end we could
drive down waiting times from six to two
weeks and the nice side effect of course
if you can scan more patients especially
if you're in private practice you can of
course generate more revenue this is an
example of how we can use AI we call
this brilliant radiology imaging
insights in radiology departments
and the final level where we can use AI
is the hospital level or I said the
network level and we call this command
center this looks like a NASA control
room but it's not this is inside a
hospital and we are using you know
predictive analytics to manage patient
flow to manage patient experience in
emergency departments and on the ICU
this is an example from the UK from
Bradford where we recently opened a
command center we call this command
center we have more than 10 command
centers in the u.s. up and running I
remember when I was a resident in
radiology I did one year of internal
medicine and and so I I was in the
emergency department and again it's in
the middle of the night and you have to
find a bed for a patient so we used to
call the First Ward and the nurse would
tell you sorry we are full you would
call the second Ward and it was really
tough to find a bed now you have full
transparency you can see where are where
are available beds where are clean beds
and then you can really optimize and you
know the usage of of beds and you can
translate this there is data from the US
that you can add virtual beds just by
better using clean or available beds so
this is a very interesting concept
it's called command center and hospitals
and really really like this to really
manage patient flow in the hospital so
now I would like to come to a very
important and point GE is a big company
so GE has like three hundred thousand
people working for GE GE Healthcare has
like more than fifty thousand employee
but if you do the math the majority of
people is outside G so also the majority
of smart people is outside G so we need
partnerships we are looking for
partnerships worldwide to develop
applications and also we need partners
to tell us if if the things we are so
excited about that we develop are really
clinically useful sometimes our
engineers are so enthusiastic they think
they have developed something great but
in the end there there is no need right
for it that is why we need partnership
partnerships and we need the user
experience we need we need the partners
worldwide and with regard to the
application development we are looking
for data partnerships in Europe also in
the u.s. of course but more and more
also in Europe and there is not one
partner for the entire field of
application development so we have a
partner where we develop this
pneumothorax app together
this was UCSF we have another partner
and and where we are looking for em are
of the heart and how to apply ai there
so very specifically for certain
indications we are looking for
partnerships so if you think of the
future and the impact of digitization on
future jobs so sometimes I'm asking
myself so what does it need to thrive
and to survive and to have a good career
in the future we all know about the IQ
you know the intelligence quotient and
we also know about EQ emotional
intelligence but there is a new term
that I would like to introduce to you
and this is TQ TQ is the technology
quotient meaning how open are you how
open are you to embrace new technologies
or are you more like the person I've
done it v the
thirty years like this I will not change
of course probably not everything that
is going to be developed in the end
turns out to be useful but the
technology quotient really shows your
ability to adapt to to the digitization
that is happening around you and if you
look at future jobs I mean on the one
hand we know that in the coming years
every second job will probably be gone
due to digitization on the other hand
there will be new jobs coming which we
probably don't have a clue right now
what these jobs will look like but if
you think you know of the of the medical
minoo area you can think we will need
health data analysts we would probably
need someone who guides us through this
jungle of all the data we will probably
have you know prevention specialists who
really use data and try to do predictive
analytics so there are a lot of probably
jobs emerging and I'm sure there will be
and I mentioned Google earlier I did a
Google search
and I typed in a I will replace and
there you can see you know the the
answers that Google gave me jobs doctors
humans lawyers okay
and then also radiologists I'm biased
I'm a radiologist myself so let's ask
the question will a I mean the end of
doctors and if you think about what a
doctor or especially a radiologist is
doing I think it's a complete you know
misunderstanding of what radiologists
are doing we do much more than just
looking at images just think of the
exciting field and growing field of
interventional radiology where you
really work with the patient and within
the field of interventional radiology
interventional oncology it's the fastest
growing field and in
radiology or for example radiologists
they sit in tumor boards they discuss
cases with other colleagues these are
all tasks I think and there are not easy
you know to be taken over by an AI on
the other hand I think it's clear
AI can do a lot of great things just
think of repetitive tasks or quite
frankly boring tasks measuring you know
lesions in the lung 30 known Lange
metastases if AI can do the job it's
great because it frees up some time for
the radiologist to look at more
sophisticated cases or to actually also
talk to the patient so I firmly believe
that you know when we look at this that
AI is there it's not science fiction
it's science fact we have to deal with
it but I think and it offers a
tremendous opportunity and if we use AI
to make a better diagnosis to make a
faster diagnosis on the other hand think
think think of it you know would you
like to sit in a plane without a pilot
the autopilot has not replaced the human
pilot but has augmented the capabilities
of the pilot at almost every Airport for
sure in Europe you can automatically
take off and land you know and with an
autopilot but I mean come on who would
like to sit in a plane without without a
pilot I like this analogy because the
radiologist of course is still is still
needed and as I said AI per se will not
replace the radiologist but what I also
say and firmly believe is that
radiologists who do not embrace this
technology in the end will we will be
replaced by those by those who do so let
me summarize
artificial intelligence is really here
and it is here to stay it will not go
away love it or hate it it will not go
away I think it it really can help us to
see more to diagnose disease faster with
a higher accuracy and it will really
help to re-establish a human connection
between the patient and the doctor so it
will help really to humanize to humanize
and rate rate radiology so in the end my
suggestion would be to responsibly
embrace AI and not fear it and with that
I'd like to thank you very much for your
attention
[Applause]
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