The document discusses the role of artificial intelligence (AI) in radiology, highlighting its potential to enhance diagnostic accuracy and improve workflow efficiency while recognizing inherent challenges such as data quality and user trust. AI can assist radiologists by taking over repetitive tasks and providing second opinions, ultimately aiming to improve patient outcomes. However, successful implementation requires addressing concerns related to user experience, financial viability, and robust performance metrics.

1. Artificial
Intelligence in
Radiology

2. Artificial Intelligence in
Radiology: Opportunities,
Challenges, and
Implementation
Presented by: Dr. Vrishit Saraswat

3. Contents
Introduction01
What is artificial intelligence?02
How can AI help the radiologist03
How can AI help the radiologist help
patients
04
Current challenges for AI in radiology05
Bibliography06

4. Introduction
•Worldwide interest in artificial
intelligence (AI) applications, including
imaging, is high and growing rapidly,
fueled by availability of large datasets
(“big data”), substantial advances in
computing power, and new deep-
learning algorithms
•AI surveillance programs may help
radiologists prioritize work lists by
identifying suspicious or positive cases
for early review.
•Predictions have been made that
suggest AI will put radiologists out of
business. This issue has been
overstated, and it is much more likely
that radiologists will beneficially
incorporate AI methods into their
practices.
•Success for AI in imaging will be
measured by value created: increased
diagnostic certainty, faster turnaround,
better outcomes for patients, and better
quality of work life for radiologists.
•AI offers a new and promising set of
methods for analyzing image data.
Radiologists will explore these new
pathways and are likely to play a
leading role in medical applications of
AI.

5. What is artificial intelligence?
What is artificial intelligence and
how does it work?Depending on the context, several definitions for
artificial intelligence can be used. Many of these
definitions link human behavior to the (intended)
behavior of a computer. In the case of radiology
these definitions do not quite cover the scope of
AI as there are many situations where AI
exceeds human capabilities.
VISION
In radiogenomics, for example, we link genetic
information to what we see on medical images,
enabling us to predict the presence or absence
of genetic mutations in a tumor which can be
used to determine further diagnosis and
management
MISSION
“a branch of computer science
concerning the simulation of
intelligent human behavior in
computers”.
.
Definition 1:
a branch of computer science dealing with the acquisition,
reconstruction, analysis and/or interpretation of medical
images by simulating human intelligent behavior in
computers”
Definition 2:
Artificial intelligence is a field of
science, with machine learning being
an important sub-field, and deep
learning is a sub-field of machine
learning.
.
Definition 3:

6. Modern Portfolio
Presentation
Whenever AI is discussed, words
like machine learning, deep
learning and big data get thrown
around... but who knows his
machine learning from his deep
learning? Let’s get a clearer
picture of some of these
buzzwords. How do AI, machine
learning and deep learning relate
to one another? A schematic
overview of the field as a whole is
shown in Figure 1 .

7. Radiologists are extremely busy healthcare
professionals. They cannot afford to make any
mistakes. They need to interact with a wide
range of referring physicians; neurologists,
urologists, orthopedic practitioners, the list goes
on. They need to be sharp, always. What can
AI bring these stretched radiologists and make
them even better at what they do?
How can AI help
the radiologist?

8. What are the benefits of AI in radiology?
AI is not good at everything. At least not yet.
What are currently the best tasks to hand over
to AI? Tasks for which we have loads of data
available, that are fairly straightforward and do
not require combining a lot of different input.
Hence the simple routine tasks radiologists do
a lot. Usually this concerns the more
monotonous tasks, in other words the tasks
that radiologists find cumbersome.
Pick up repetitive routine tasks
Many AI solutions are focused on providing extra
information. This can be by quantifying information
enclosed in an image, where it is currently only
reported in a qualitative way. Or the software can add
normative values, allowing physicians to compare
patient results to an average based on a cross-section
of the population. The difficulty with this benefit is that
we do not always know yet how to handle this extra
information
Provide a more differentiated diagnosis
Even the best trained, most experienced radiologists might
differ in their diagnosis sometimes. Well rested in the
morning, something different might catch the attention than
after a long working day. Additionally, different radiologists
might emphasize different aspects in their reports. This can
be tricky for referring physicians, as they need to take into
account these variations when synthesizing all the
information they have, before coming to a final diagnosis.
AI software has the ability to decrease or even eliminate
this variability between radiologist reports.
Eliminate inter- and intra-observer variability
Having an AI algorithm run in the background offers
an easy way for obtaining a second opinion. The
algorithm results can serve as a simple backup check
on the diagnosis of the physician. An additional benefit
of having AI software running as a second opinion is
that it allows the radiologist to gradually get used to
working with AI and build trust as they see that it adds
value.
Offer a second opinion
There are several ways AI can advance the performance of radiologists even further. In this section we will discuss a few of these approaches.
This list is not exhaustive. There are many more ways for AI to benefit the radiologist such as:

9. How will AI realize
these benefits?
There are many tasks AI can perform in the context of
radiology. Some tasks will require just a medical image
as input and will base the analysis purely on the pixels
(or voxels). Others will go one step further and will
combine radiological images with information obtained
from other sources
Using only the image as input
AI that uses only a medical image as input, will deliver results that are
mostly similar to what radiologists otherwise would do manually. For
example, automatic segmentations of specific organs can be done
manually (e.g. liver and HCC segmentation to determine whether a
resection can and should be performed). However, these type of
analyses are very time consuming and therefore very suited to
“outsource” to an algorithm. Another example is the quantification of
specific distances (e.g. automatic measurement of RECIST scores).
Again this can be done manually, but many radiologists experience this
as monotonous task, making it a suitable candidate to get some AI
help.
Adding other information from other patient exams
Combining medical images with other information can lead to insights
that are not always easily to obtain for radiologists. These types of
analyses are usually considered more futuristic. For example, by linking
image data to pathology lab results it is possible to let an algorithm
derive pathology information from a medical image. Another example is
an algorithm that extracts genetic data from images without having
access to genetic markups of a patient.
A different type of analysis is adding normative information. For
example, by comparing patient organ volumes to the average of the
population. This can be useful in dementia research (comparing
volumes of specific brain structures to a normative database) or in case
of splenic enlargement.

10. How can AI help the radiologist help patients?
Each diagnostic process aims to realize the best patient outcomes. Medical imaging is increasingly part of the
diagnostic chain and should therefore be aimed at the exact same end goal: benefiting the patient. Hence for each
AI solution used by radiologists to assess images, we should do the litmus test and ask “At the end of the day,
does this software benefit the patient?” Simply put, you can think about patient benefits along two axes: the quality
and the efficiency axis. We will discuss both below.
Quality increase for better patient outcomes
AI offers great potential to increase quality of current
image readings. For example, by performing analysis
that are currently not performed because those are too
time consuming for radiologists to execute manually. An
example is volumetric measurements of organs, where
manual delineation is too demanding time wise, but
could improve the accuracy of the diagnosis.
Additionally, AI is an important enabler of precision
medicine. As more patient data becomes available, we
can determine in a more detailed way what information
implies certain treatments leading to better patient
outcomes. Another step in the process that can
improve with some AI influence is patient
communication. This is not necessarily directly the field
of the radiologist, however, radiologists can deliver
easier to understand reports to the referring physician
that can facilitate patient conversations.
Efficiency improvement to benefit the patient
Quality of care is extremely important, however, if the
diagnosis takes too long, great quality is of no use.
Therefore, quality should always be combined with efficiency.
AI can help increase efficiency in several ways. It can help
speed up the diagnosis process by automating tasks that are
time consuming when performed manually. For example,
RECIST score measurement can be a good candidate for
acceleration by automating the process. Another possibility is
to help the radiologist prioritize urgent cases. Which imaging
exams should the radiologist assess first? AI can do a first
assessment and move cases up the list if necessary.
02
AI

11. Will AI take over radiologist jobs?
It will pick up routine tasks which are
experienced as cumbersome by a lot of
radiologists. Yet, radiologists have a much
more differentiated job than these type of
tasks alone. Radiologist jobs will change,
but they will not disappear
.
it will not take over radiologist jobs.
However, it most certainly will take
over some radiologist tasks. It will
support radiologists by performing
automatic measurements which are
currently very time consuming
Simple answer, No,

12. What are current challenges for AI in radiology?
A smooth user-experience
A business case that adds up
The right performance
metrics
A sufficient amount of
quality labelled data
Dealing with a 3D
reality
Non-standardized
image acquisition
CHALLENGES
The sky seems to be the limit when it
comes to applying AI in radiology. But
for now there are still quite some
challenges to overcome before AI will be
widely applied and fully adopted in the
radiology workflow of which are:
The user's trust

13. What are current challenges for AI in radiology?
A sufficient amount of quality labelled data
Within the medical field, access to large high-quality labelled datasets for training is not straight
forward. Other general databases are extremely powerful because they include a vast amount of
images which are accurately labelled. Comparing the typical medical imaging dataset of approximately
1000 images to a non-medical database which can contain up to 100 000 000 pictures, it can only be
concluded that the volume available is clearly still several orders of magnitude behind. A way to
overcome this problem is using augmentation.
Figure : Big data can consist of many different types of data.

14. What are current challenges for AI in radiology?
Dealing with a 3D reality
The most successful deep learning models are currently trained on simple 2D pictures. CT- and
MRI-images are usually 3D, adding an extra dimension to the problem. Conventional X-ray
images may be 2D, however, due to their projected character, most of the current deep learning
algorithms are not adjusted to these images either. Experience needs to be gained with applying
deep learning to these types of images.
Non-standardized image acquisition
Varying scanner types, different acquisition settings… Non-standardized acquisition of medical
images creates a challenging situation for the training of artificial intelligence algorithms. The
more variety there is in the data, the larger the dataset needs to be to ensure the deep learning
network results in a robust algorithm. A method to tackle this barrier is to apply Transfer Learning,
which is a pre-processing technique aimed to overcome scanner and acquisition specifics.

15. What are current challenges for AI in radiology?
A smooth user-experience
One of the most frequent comments radiologists mention
are regarding their non satisfactory experience with
current radiology software. Why? Because it is generally
very user-unfriendly. It requires too much waiting time, too
many clicks, and once you are in the program you cannot
live without the manual by your side.
Creating a user friendly software is a must for AI
companies that want their software to be used in the clinic.
However, it is not as straightforward as it sounds. Many
applications are developed tech-first, meaning a company
starts with an algorithm and then turns it into a product.
This is not necessarily a bad approach, you’re certain
you’ve got a working algorithm, but testing the user-
friendliness of your product should be part of the product
development process, preferably from the start, to ensure
radiologists will start and keep using it in the clinic.
Figure : AI applications should ensure a smooth user
experience to encourage adoption

16. What are current challenges for AI in radiology?
A business case that adds up
How will the financial picture play out? Will hospitals have to allocate budget? Will the bill
eventually be presented to the patient? Or, the scenario we are aiming at, will AI pay for itself? It
is unlikely that AI radiology software will, in its current state, be able to fully support itself, mostly
due to the upfront investment that needs to be made. In this situation it is only obvious that the
investor wants to know what to expect on the long run, hence AI companies need to have a clear
view on how their software will financially benefit hospitals in the future: will it save physicians
time? Will the hospital be able to shorten waiting times and because of that help more patients?
Will diagnosis accuracy improve and cause savings at a later stage in the process? Are there
reimbursement codes available for the specific analysis
The right performance metrics
Every scientist will confirm you need to be clear on what you measure, how you measure it and,
probably most important, why you measure it. AI in radiology is no different from any other field of
research. AI companies need to be very clear on their performance measurements. Often used
metrics are accuracy, precision, recall, etc. However, these metrics do not always apply. Accuracy
is calculated using the amount of true positives, true negatives, false positives and false
negatives. In case of automated segmentation, there is no straight forward interpretation of a true
positive or a false negative. Hence metric selection is of utmost importance if you want to paint

17. What are current challenges for AI in radiology?
The right performance metrics
Every scientist will confirm you need to be clear on what you measure, how you measure it and,
probably most important, why you measure it. AI in radiology is no different from any other field of
research. AI companies need to be very clear on their performance measurements. Often used
metrics are accuracy, precision, recall, etc. However, these metrics do not always apply. Accuracy
is calculated using the amount of true positives, true negatives, false positives and false
negatives. In case of automated segmentation, there is no straight forward interpretation of a true
positive or a false negative. Hence metric selection is of utmost importance if you want to paint
the right picture on how your software performs.
The user's trust
However, possibly the most important challenge AI radiology companies are facing is the lack of
trust in artificial intelligence when it comes to answering questions related to medical image
analysis. AI is often seen as a “black box” of which it is unclear how it exactly came to its answer.
How can we ease the adoption of AI and strengthen the user's trust? There are several ways of
doing this: with scientific research, installing the software in the hospital and test run the
application. If AI companies want to survive in the field of radiology, they should invest in gaining
the user’s trust.

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19. Thank You