The future of artificial intelligence in radiology: Prof. Dr. med. Mathias Goyen

Summary

The 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

00:00

🧠 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.

05:03

🛠️ 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.

10:06

🚀 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.

15:07

🏥 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.

20:10

🤝 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.

25:13

🌐 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.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence processes by machines, especially computer systems. In the video, AI is a central theme, with the speaker discussing its future role in radiology and healthcare. The script mentions AI's ability to handle the vast amount of medical data and its potential to reduce medical errors, showcasing how AI can be integrated into medical imaging and workflow optimization.

💡Radiology

Radiology is a branch of medicine that involves the use of imaging techniques to diagnose and treat diseases within the human body. The video's speaker, a radiologist, emphasizes the impact of AI on radiology, particularly in analyzing medical images and assisting with diagnoses. Radiology is highlighted as a field where AI can significantly improve efficiency and accuracy.

💡Healthcare Data

Healthcare data encompasses all the information generated within the healthcare sector, including patient records, medical images, and research findings. The script illustrates the exponential growth of healthcare data, which is mind-blowing and challenging for professionals to manage. AI is presented as a solution to handle and analyze this data effectively.

💡Medical Errors

Medical errors refer to mistakes or oversights that occur in healthcare settings, potentially leading to patient harm. The video script points out the staggering number of medical errors that occur annually, with AI proposed as a tool to reduce these errors by improving data analysis and diagnostic accuracy.

💡Healthcare Workforce Shortage

Healthcare workforce shortage describes the insufficient number of healthcare professionals to meet the demand for care. The script mentions a global shortage of healthcare workers and projects an increase in this gap by 2035. AI is discussed as a means to assist and augment the capabilities of the existing workforce.

💡Personalized Medicine

Personalized medicine is an approach that tailors medical treatments and therapies to the individual characteristics of each patient. The video discusses the shift from a one-size-fits-all approach to a more individualized strategy, using the example of Herceptin-positive breast cancer treatment to illustrate how personalized medicine can improve patient outcomes.

💡Wearables

Wearables refer to electronic devices that can be worn on the body to track health metrics, such as physical activity or heart rate. The script touches on the potential of wearables in healthcare, suggesting that they could be transformed from fitness tools into medical devices that monitor patients more closely.

💡Exponential Growth

Exponential growth describes a process of rapid increase where the rate of growth accelerates over time. The video uses the analogy of walking steps around the globe to explain the concept of exponential growth in the context of healthcare data, emphasizing the need for AI to manage this overwhelming increase.

💡Digital Twin

A digital twin is a virtual model or replica of a physical object or system. In the script, the speaker mentions the use of digital twins in aviation for predictive maintenance of jet engines, illustrating how AI can analyze data to predict and prevent mechanical failures.

💡Command Center

A command center in the context of healthcare is a centralized hub for managing and optimizing various hospital operations, such as patient flow and resource allocation. The video describes how AI and predictive analytics are used in command centers to improve hospital efficiency and patient experience.

💡Technology Quotient (TQ)

Technology Quotient, or TQ, is a measure of an individual's ability to adapt to and embrace new technologies. The speaker introduces TQ in the video as a crucial factor for thriving in a digitized future, where AI and other technologies will play a significant role in job roles and career development.

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]