Greetings and welcome to EHA Unplugged, the official podcast
channel of the European Hematology Association EHA.
Hi everyone, My name is Wilson Lim, a medical writer for EHA.
I'm sitting here today with Professor Gregor Horman to talk
about AI, artificial intelligence in hematology.
Professor Orman, would you introduce yourself please?
Yeah. Hi.
My name is Gregor Herman. I'm a clinical pathologist or
specialist in laboratory medicine working at the MLL of
the Munich Leukemia Laboratory in Germany.
The M&L is one of the largest laboratories focusing on
patients with leukemia and lymphoma and diagnosis of bone
marrow and blood samples. So when we talk about artificial
intelligence, AI, the first thing that comes to mind is all
the large language models such as ChatGPT and and Gemini.
Many, many people are unaware of the other models that can be
applied that are applied in healthcare.
Can you share with us some examples of that?
It's a huge spectrum of AI to be applied in healthcare, for
example, where we are mainly focusing on is of course using
AI for our diagnostic processes. And there we are mainly applying
a particular kind of AI machine learning or more specifically
deep learning, where we try to get the information out of our
data and use them in a scientific way.
With those AI machine approaches, for example, we are
doing soda morphology or we are doing chromosome on Banach
analysis. Both of those techniques focus
on imaging. So our technicians, our soda
geneticists, our haematologists have to assess those images and
tell what kind of cells are these, what type of chromosomes
are these? And we can use here deep neural
networks, a kind of machine learning approach to align those
pictures to the classes of cells or the classes of chromosomes.
So we extract the features out of the picture and then have the
network aligning the cell to a particular class.
For example telling this is a blast and this is very helpful.
As to the cells are pre annotated, of course you need
all the data digital. First of all, we have to
photograph all the cells from the peripheral blood smear or
from the bone marrow smear or have the metaphases digital
available and then we can apply those AI models to help us in
the classification of the particular cells.
This is nothing that the EI is doing alone.
This is definitely a Co working between the EI and our trained
personal. So each and every cell is then
reviewed by a trained medical personnel and then we have a
final report to be assigned. Still, this addition of AI was
very helpful in our processes, in particular inside genetics.
This has really streamlined our reporting.
We are much faster now. We can deliver the report faster
to the patient and therefore help the patient care directly.
So does AI help? Yes.
So we can use health data to predict and help us with certain
tasks. So what I've just elaborated on
was a rather easy task. Just tell me what is this cell?
What is this chromosome? So we have a picture as an input
and the classification as an output, rather straightforward.
Of course, we can also ask more complex questions.
For example, is this picture associated with a certain
genotype or is this picture associated with a certain
outcome of the patient? So then we are talking about the
CS classification prognostication.
EI can also be used for this. Again, we typically are then
using machine learning approaches.
We can use this with supervised or unsupervised machine
learning. And a particular unsupervised
machine learning also helps us to identify new classes of the
CS where we would not have thought about.
We have so much data now available in the area of
genomics, where we struggle as humans to interpret all those
data, to find patterns in this large field of data.
And here AI is helpful to point to certain classes that are
biologically similar depending on the genotype, on the other
features. And then we as the humans of the
medical doctors have to figure out if those classes are then
meaningful in terms of treatment, in terms of
prognostication, and how to deal with this.
From all accounts, this also leads to a huge improvement in
efficiency. How long would you say it takes
to process for example image and karyotyping?
Would you say 10 seconds more or less?
Even faster. Now we are in the range of one
second. So it's really just get it
started and it's done. So that's not a bottleneck.
The system is really fast. And to get this into context, a
well trained technician who is doing this karyotyping leads
about one minute for one metaphase and you have to do
multiple of those to assess one case.
So you typically need half an hour to assess a case.
And now this is pre prepared. It still takes some time, but
for reviewing and this is much faster than doing everything
from the scratch. Do you think we can still
improve this? There's always room for
improvement, so in the past we have been focusing on normal
chromosomes. The mast majority of those are
normal, and we also can assign a numeric operation, meaning just
one chromosome in addition of 1 chromosome missing.
This nicely works, however, when we're talking about structural
operations, for example, a translocation that leads to a
fusion of the chromosomes. BCR Able would be such an
example with the philadelphic chromosome.
This needs to be specifically trained to also recognise those
structurally abnormal chromosomes, and that's what
we're currently doing. We're training the system with
the most frequent and most relevant structural operations
to also have them automatically key assigned.
You mentioned morphology. Are there any morphology AI
models as advanced as the Kyo Typing 1?
Well, the current typing was the first one out there and it is
really something that we have in place in our routine diagnostic
application for a couple of years now.
Therefore, we have a lot of experience with this and also
other labs are already using this approach and we know it's
really helpful also in in their hands in the field of
sodomorphology. We are a little bit beyond this.
We need to imply this in our own lab right now.
This is perfectly fine. And we're currently in this
transition phase to see how robust the workflow is to be
also applicable for example, in other labs.
So if any of our listeners wants want to get to know more about
the Kyo typing process, it's AI. For example the benefits or what
it take to set it up? How and where can they find more
about it? You can contact us, we can get
in touch. There is always room for
collaboration. We have a designated AI team in
our laboratory and currently there are five people working
full time on AI applications in this diagnostic field.
In medicine. We are enlarging in this team
right now up to 10 people that will work at AI and for example,
with the Carrot typing model. This has been a cooperation with
Meta systems, is also available via them directly.
So shifting gear a bit here, can you tell us a bit about how we
can use AI to determine patient prognosis?
Yes. So when we're talking about
prognosis and hematology, we now rely on rather well simple
models because we need to handle those.
For example, if you think about acute mild leukemia, then we
have the ELN certification in three different risk groups of
this of utmost importance for treatment of patients with AML.
The patients are assigned to this risk groups based typically
on one marker 1 genetic operation found inside genetics
or molecular genetics and this makes the risk group for this
patient. This is straightforward
physician can deal with this type of information perfectly
fine. However, we are losing kind of
complexity because in many instances we have combinations
of multiple mutations and together with other operations
and to assess this type of complexity here AI can help.
There are a number of studies out there that just asked are
there biological subclasses within AML that can be assigned
to a different biology or to different outcome?
And this more evidence based specification taking into
account the complexity of the disease will be an interesting
way to move the field forward. So the principle behind this is
it applicable more widely, for example to MDSMPNS etcetera.
Yes, it's the principle is applicable for all those types
of diseases. For MD's, we now have the IPSSM
that has come out in parallel. There have been multiple
publications on other approaches, including also EI
for certification of those patients and machine learning is
an issue there. Moving forward, to what extent
do you think you'll be able to implement this in your
laboratory, for example? Yeah, they can be implemented
and they have already been implemented to some extent.
The important thing is you have to carefully validate your
model. You need to know the
performance, how well is it working, what are problems, what
are cases where it doesn't work. And this is really careful
validation process that is of utmost importance before you can
apply this into a clinical practice.
When we are talking about what we are currently doing, this are
rather narrow questions that we are asking, for example, how to
assign the class of the cell. And this is what we will call a
uni model AI, just one input and one output format.
In the future, there are now proposals and models out there
that can deal with multiple formats multimodal AI.
This will be a very interesting topic to see how the field is
moving with this. We are not there as a current
state to implement this in routine practice, but this will
be of utmost importance to move forward.
So what is synthetic data? It's a pretty hot topic at the
moment. Would you be able to explain
more about it? Yeah, that's a interesting and
rather new field within generation of data with AI.
So large language models are kind of generative AI, and this
is also something a little bit in this direction.
So we're not just analysing data, but we're producing data
with this. What does this mean?
You have a large data set. Big data is still an issue
there. You have a large data set of
real patients for example, and you then train a model to
produce patients data sets that are like the real ones but not
perfect copy of the real ones. So we've slide modifications and
those new data sets are then called synthetic data sets.
What are pros of this of this approach?
You have better patient privacy as the real patient data are
then not needed for sharing any more.
You can share those slightly modified data, but we can also
augment the data set, increase the number at least to some
extent and to make some studies more powerful with this.
That's an emerging field. We will have to see for what
applications is really useful in clinical practice and where the
limitation of this technique are.
So for all our listeners who was also interested in synthetic
data, we will have a specific podcast on that with Professor
Matteo de la Porta. If you're interested, please
keep an eye out for today episode by subscribing to the
EHA Unplugged Podcast channel. So taking all the things that
you've spoken about, along with the language models, being able
to suggest reports and other communications, this is moving
towards an AI doctor. What is your take on this?
Well, we should not overestimate what AI is currently able to do.
So those models are helpful in many instances.
They can well help us get rid of the boring stuff to do and then
focus on the complex things. Right now, AI will not be able
to solve all the very complicated cases and really get
up with much new information on a single case.
This is really something where the human is still in the front.
However, it is a matter of working with the eye, not
against the eye, so it should really help us in our process
and not replace what we are doing in this combination of
human intelligence together with the eye is definitely the way to
go for it. So what you're saying is that we
shouldn't be afraid of AI, but rather we should embrace this
change and move forward with it? Absolutely agree.
So don't be afraid, try to learn about AI and here EHA can be
very helpful with those training activities, trying to learn
about their pros, the cons, the limitations, how you can use AI,
how you can use AI in a way that was really helpful for you in
your daily practice. And yeah, just you this
opportunity, it's a great area where we're leaving right now
and we should not only focus on negative sides, but really get
into the doing with this. There was a wonderful quote at
the end of your talk here at the EHA 2024 Congress.
Would you be able to share it again with our listeners here?
Yeah, the quote is from Eric Topple, one of the most famous
physicians dealing with AI. And he said AI will not replace
physicians, but physicians using AI will replace physicians not
using AI. It's a very interesting quote,
yes. Thank you very much, Professor
Holman for this wonderful talk and also thank you for everyone
listening. If you're interested in more
podcasts, do stay tuned and check out our EHA Unplugged
channel. Until then, have a nice one.