Dr Natalie Banner, Ismael Kherroubi García and Francisco Azuaje: Can Artificial Intelligence accelerate the impact of genomics?

On this episode, we delve into the promising advances that
artificial intelligence (AI) brings to the world of genomics,
exploring its potential to revolutionise patient care. Our guests
discuss public perspectives on AI in genomics and address the
ethical complexities that arise in this rapidly evolving field.
Gain valuable insights into the future landscape of genomics and
AI, as our experts discuss what to expect on the horizon. 


Our host Dr Natalie Banner, Director of Ethics at Genomics
England, is joined by Ismael Kherroubi García, member of the
Participant Panel and Ethics Advisory Committee at Genomics
England, and Francisco Azuaje, Director of Bioinformatics at
Genomics England. 


 


“So, AI is already driving the development of personalised
medicine for both research and healthcare purposes. [...] In the
context of healthcare, we are talking about AI tools that can
support the prioritisation, the ranking of genomic variants. To
allow clinicians to make more accurate and faster
diagnosis.” 


 


You can download the transcript or read it below.


Natalie Banner: Hello, and welcome to the G Word. In the past few
years, artificial intelligence, or AI as a shorthand, has taken
centre stage in the headlines. Sometimes for really exciting,
positive reasons about the potential to drive improvements for
society, and sometimes because of its potential risks and harms.
 These discussions and stories can sometimes seem like
they're straight out of science fiction. There are a lot of
questions, excitement, concerns about the societal impact of AI,
so not just looking at individual patients, but that broader what
does this mean for society?  


Ismael Kherroubi García: My somewhat hot take is that AI only
accelerates societal impacts that genomics research and
healthcare can have. So the impacts, of course, will be diverse
and complex and quite widespread, especially given the quite
nuance and sometimes difficult to understand areas of genomics
and artificial intelligence.  But the key takeaway from what
I want to say is that it only accelerates the impacts of genomics
and healthcare. So if we take genomics research to promote human
flourishing, ideally, artificial intelligence will also only help
further human flourishing.  Conversely, applying artificial
intelligence tools to genomics research can help perpetuate
certain stereotypes and related harms.  


Natalie Banner: My name is Natalie Banner, and I'm the Director
of Ethics at Genomics England. On today's episode I'm joined by
Ismael Kherroubi García, member of the Participant Panel, and
Ethics Advisory Committee at Genomics England. And Francisco
Azuaje, Director of Bioinformatics at Genomics England. In
today's episode we aim to cut through the hype and hyperbole and
explore the real possibilities for AI within the domain of
genomics and healthcare. We'll look at how AI tools and
techniques have been used to date, and what the future holds,
considering both the benefits and challenges faced in the
genomics ecosystem. If you enjoy today's episode, we'd love your
support. Please like, share, and rate us on wherever you listen
to your podcasts.  


AI is in the news an awful lot, and not always for good reasons.
There are many big and small tech companies that are exploring
the use of AI in all walks of life, from finance to retail, to
healthcare. And it's not always clear what AI means in these
contexts, where it actually has the potential to really help
people, drive improvements to healthcare and society, for
example.  But there are some exciting stories, so recently,
Genomics England undertook a collaboration with DeepMind on their
AlphaMissense tool, and that sought to classify the effects of 71
million missense mutations in the human genome. So it could
process data at a scale and a speed far faster than any human has
ever been able to before. So there's an awful lot of exciting
work going on in AI, but we should emphasise that although some
of this technology is really cutting edge, a lot of the
techniques that are being used and talked about in AI, have
actually been around for quite a long time. So Francisco, if I
could start with you, can you help us understand what artificial
intelligence, AI really is in the context of genomics? And maybe
explain to us the difference between AI and machine learning, and
how they relate to one another? 


Francisco Azuaje: Sure, Natalie. AI involves the creation of
computer systems capable of performing tasks that typically
require human intelligence, such as understanding natural
language, recognising patterns in big data sets, and making
decisions. Now, machine learning is the most successful
technology within the field of AI, but machine learning focuses
on the use of algorithms that allow computers to learn from the
data and make predictions about the data without the need for
explicit programming for each task or application. 


Natalie Banner: Ismael, perhaps I can turn to you. What do you
see as the primary motivations or reasons for incorporating AI
into genomics research and, indeed, into healthcare? 


Ismael Kherroubi García: So I think it's already been mentioned
and focusing on genomics research because challenges, the
enormous amounts of data that are required to shift through and
analyse and get insights from.  So one number that's worth
just mentioning is that the human genome is made up of 3.2
billion base pairs, As with the Ts and the Gs with the Cs in our
DNA. And one way to put that 3.2 billion, with a B, in terms we
might understand, is to say that to list all of those letters we
would have to be typing 60 words a minute, eight hours a day for
about 50 years, that's how enormous just one human's genome is.
 And I kept looking for other ways of depicting just how
enormous this data set is, and it turns out if you uncoil those
strands that we usually see depicted when talking about DNA, if
we uncoil them for one person, we would have a string about 67
billion, again with a B, miles long for each person, that's
roughly 150,000 round trips to the moon.  So, again, this is
just one person, those numbers are enormous.  


It's also worth considering the role technology has had to play
in enabling genomic research. So if we look back at maybe a very
significant catalyst for genomics research, the human genome
project which started in 1990, they took 13 years to sequence one
human genome. Now, what we're talking about is estimating that by
2025 genomic-related data will be up to 40 exabytes of data. Now
I didn't even know what an exabyte even was before this podcast,
so I did look it up, that's about a billion gigabytes, I
definitely don't know how to even begin to imagine what 40
exabytes even means.  Bringing it a bit closer to home, I
try to figure out how many copies of Doctor Who we would need to
make 40 exabytes of data, I found that Doctor Who is roughly 850
gigabytes, I found this on Reddit, very scientific. And the
number is then 40 exabytes over 850 gigabytes, that's roughly 47
million copies of all the decades of Doctor Who media is what's
necessary to reach the amount of data we expect from genomic
research within a couple of years. So we need a technology
capable of analysing the equivalent of 47 million copies of the
entirety of Doctor Who, and currently, as you've both mentioned,
AI provides the best way we have to do this. 


Natalie Banner: Wow! So we are talking absolutely vast amounts of
data.  And I do love the analogies there, it's very helpful
to actually sort of bring it home and make it real.  So
we're talking vast amounts of data and currently it feels as
thought AI may be the best way to try to analyse and explore that
scale of data.  So given that's what we're talking about in
genomics, in what ways is AI currently being applied in the field
of healthcare and genomics?  Francisco, I'm wondering, can
you give us any examples of how Genomics England is integrating
AI models and tools into its research efforts? And I know
particularly we have a programme of work exploring multimodal
data, can you tell us a little bit about that?  


Francisco Azuaje: Absolutely. But first of all, just to give you
an overview of the type of applications in research and
healthcare, right now AI offers opportunities to develop tools
that are needed to support interpretation of genomic variants,
and the relationship between those variants and medical
conditions, drug responses.  AI is also a powerful approach
to supporting the detection of diseases and some subtypes of
these conditions, and matching those conditions to treatments,
using different types of data, in the clinic this is happening
already in the clinic, and examples of data include medical
images, clinical report, electronic health records. So AI is
already driving the development of personalised medicine for both
research and healthcare purposes.   


Now, at Genomics England we are investigating the use of AI to
support a number of tasks with potential impact in both research
and healthcare.  In the context of healthcare, we are
talking about AI tools that can support the prioritisation, the
ranking of genomic variants to allow clinicians to make more
accurate and faster diagnosis. You mentioned the multimodal
programme at Genomics England, as part of our mission to enabling
research, we are developing tools and applications to help
researchers extract information from different modalities of data
or data types. In this context, AI plays a crucial role to deal
not only with the size and the volumes of this data, but also to
allow the meaningful extraction of useful information with
clinical value based on the combination of different data sets.
And that's a complex challenge, that only AI can approach. Here,
we're talking about large, diverse, and complex data sets coming
from different types of clinical imaging modalities. We are
talking of course about genomic data, clinical reports, and in
general any information that is included in the patient's medical
health record. 


Natalie Banner: Thanks, Francisco. And can you talk a little more
about the specific tools or projects you're working on at the
moment in multimodal? 


Francisco Azuaje: Absolutely. So, in the case of multimodality,
we are talking about applications that aim to improve the way we
connect any of these data sources, including imaging and
genomics, with clinical outcomes. For example, how to improve the
way we predict not only a diagnostic type, but also how that
information can be correlated with the potential response of a
patient to a particular therapy. Or a prediction of the potential
evolution of that patient within a particular subtype of
condition or phenotype. To do this we rely on a type of a machine
learning technique called deep learning, just very briefly, deep
learning models are again a branch of AI, so within machine
learning, these models are very powerful tools that apply deep
neural networks. These networks consist of multiple layers of
mathematical transformations that are applied to the data. And
these transformations allow them the automatic discovery of
complex patterns in this data, including all these modalities
that I mentioned before. So this is a key approach that we need
to extract useful features with diagnostic or prognostic value
from these different modalities of clinical information. 


Natalie Banner: So there's obviously a really clear focus there
on the benefits to patient, the patient outcomes, really trying
to ensure that we can create personalised medicine as far as
possible.  That every patient can have an outcome that's
kind of very much about their own particular circumstances and
condition.  So not just looking at individual patients, but
broader, what does this mean for society? Ismael, I wonder if you
can tell us a little bit, your thoughts on the questions about
societal impacts with the increasing use of AI, particularly in
genomics and healthcare more widely? 


Ismael Kherroubi García: Yeah. So my somewhat hot take is that AI
only accelerates societal impacts that genomics research and
healthcare can have.  So the impacts, of course, will be
diverse and complex and quite widespread, especially given the
quite nuanced and sometimes difficult to understand areas of
genomics and artificial intelligence.  But the key takeaway
from what I want to say is that, it only accelerates the impacts
of genomics and healthcare.  So if we take genomics research
to promote human flourishing, ideally artificial intelligence
will also only help further human flourishing.  Conversely,
applying artificial intelligence tools to genomics research can
help perpetuate certain stereotypes and related harms. Genomics
England has diverse data initiative and they show that Europeans
represent 78% of people in genome-wide association studies.
  


The challenge here is that, if we train artificial intelligence
tools on complex interrelations of mainly the genomes of people
with European ancestry, then we are over-sampling people with
European ancestry, and the findings will have very limited
effectiveness on different populations, both around the UK and
diverse populations around the world and within the UK. So whilst
artificial intelligence will have societal impacts as a general
sort of technology that can be applied to many different fields,
in the context of genomics and healthcare, I think that the
societal impacts we should really be focusing on relate with
genomics and healthcare in particular.   


Francisco Azuaje: I agree with Ismael, that the real value of AI
is not only in the acceleration of technological progress, but in
the impact at different levels of society. Including the way we
improve health in an ethical way, and also in the way we support
people to develop tools that have an impact in the way we operate
as societies and the way we relate to each other. So I totally
agree, it's more than just technological acceleration.
  


Natalie Banner: Absolutely, okay. So we've talked about the
potential societal impacts, and I mentioned at the outset that
there's a lot of hype and a lot of interesting narratives about
AI in the public domain.  Things can feel very utopian or
dystopian as an awful lot of marketing, but understandably as
well a lot of fear coming from the public perspective, especially
if you think that most people's understandings of terms like AI
have come from science fiction, for example. So, Ismael, what
concerns are there from a public perspective? Particularly for,
you know, when patients faced with the increasing use of AI and
machine learning in genomics and healthcare, and the idea that
their care or their treatment could be informed by these tools
and technologies. What kind of challenges might arise for
patients in the future as these technologies continue to advance?
And what are the perceptions like from that public and patient
perspective? 


Ismael Kherroubi García: I think you got it entirely right. The
biggest concern relates with the public perception of AI and that
perception in turn is significantly impacted by what we see in
mainstream media, be it in the news media, social media in
adverts, and so on. And unfortunately, as artificial intelligence
is usually depicted as this extremely technical field, the
conversation, the narrative is more often than not steered by big
tech, so organisations and people with very clear agendas.
 The example I want to make this case with is this open
letter, I think it was in March this year, which was put together
by a series of company CEOs, a few researchers as well, and it's
ultimately been signed by over 30,000 people. And this open
letter called for a six-month pause on what they called 'giant AI
experiments'. So this was an open letter and direct response to
the launch of ChatGPT, which is an AI-based chatbot launched by
Open AI in November 2022.   


The open letter suggests that we might, in quotes, that 'we might
be developing non-human minds that might eventually out-number
and out-smart us. And we're risking loss of control of our
civilisation'. These are extremely serious fears, and I would be
really afraid if I believed them.  So the concern here is
that the fears aren't really grounded in reality, but in common
fiction or narratives about AI.  And very quick way to see
that there's a lot of fiction around AI, if you go online, go to
your favourite images browser, and look for 'artificial
intelligence', you're going to find a lot of images of blue,
floating brains, a few versions of the terminator, robot shaking
hands with people.


There's one great image of a robot using a laptop – which always
makes me laugh. These are not informative depictions of
artificial intelligence, let alone genomics. And the risk is
ultimately that, if the general public has easy access to
unhelpful fictions about AI, then there's a great possibility
that genomics research, which is going to remain intricately
linked with AI advancements, will be perceived negatively, so
genomics services fuelled by AI will not be trusted. And
ultimately, given my stance, and I think the shared stance that
AI is necessary for genomics, who's going to pay? Well, that will
be the patient.  


Natalie Banner: So we have quite a battle on our hands, in terms
of trying to create space for those informative discussions, as
you call them, Ismael, about the realities of what AI can and
will be doing in genomics.  Francisco, how are we addressing
those kind of questions and concerns in our work at Genomics
England?  What steps do you think we can take at Genomics
England to talk more openly about the work that we're doing
involving AI to try and create space for those informative
discussions that aren't led by the hype or the fears of AI? 


Francisco Azuaje: I agree that we have to ensure that we are not
distracted from many discussions that emphasise potentially
fictional or existential risk of AI.  I think there are
valid concerns about existential risks that don't represent the
AI fictional view of Hollywood, but that really affect the way we
operate our societies. For example, existential risk for
democracies, if you have monopolies of this technology. If we
have less accountability, in terms of governance. If we have
electoral systems that do not work. So if that doesn't work it's
going to be very hard to benefit from AI within healthcare, so
that's something to be considered as well.  But I agree that
sometimes the discussion is driven by this interest in very
long-term potential scenarios. I think the key is to achieve a
balance between longer-term and near-term priorities. And in the
case of healthcare, there are many challenges and issues that we
should be discussing and addressing by now, including challenges
regarding the privacy and the respect of rights of our patients
and individuals. Concerns about the biases embedded in the data
used to build the systems, biases actually embedded in the
practices for building the systems. So these are real risks, that
in the case of healthcare and research need to be addressed now.
  


In the case of Genomics England, we are doing a lot of work that
is laying the groundwork for safer, ethical uses of AI. So this
means, for example, that we will continue doing what we do,
inspire and driven by the need to respect our patients' our
participants' privacy and rights and voices, so that's essential.
 In practice, this means that we work closely with our
Participant Panel and different committees responsible and
accountable for protecting these views and rights. From a machine
learning point of view, there are technologies and tools that are
quickly emerging that we are using to ensure that our systems are
properly designed with ethical considerations in mind.  


For example, we ensure that our data sets are of good quality,
and good quality means not only the information that we want to
use for a particular application, but also means that we identify
and quickly mitigate potential biases embedded in the data. It
also means that if we share a tool, for example, within our
Research Environment, these tools have been properly tested. Not
only for reliability but also for potential risks associated with
privacy, with biases, etc., before these tools are deployed to a
production environment or shared with the wider community. So
these are basic steps, but I think they are essential, starting
with the protection of our data and also by applying best
practice in the way we build and evaluate these models carefully
before they are deployed to a wider use.   


Ismael Kherroubi García: And that, to me, makes perfect sense,
and it's always encouraging to hear the practices around AI and
Genomics England. There is one challenge that came to mind that
you mentioned, the impact of democratic values, on potentially
artificial intelligence informing social media, that informs
electoral processes. And there's another very real, tangible
issue with artificial intelligence, which is the environmental
impact. So what's really interesting, the challenge here is that
artificial intelligence tools have significant environmental
impacts.  You have enormous data centres that need to be
submerged in water, maintained, kept cool, and we're developing
enormous algorithms, ChatGPT, and so on, that require these huge
amounts of data I mentioned earlier on. So there is this really
tricky balance between health and the natural environment, which
I don't have the capacity to even begin to think about.
  


So, I sit on the Participant Panel at Genomics England, and the
conversation often goes around how Genomics England use our data,
how our privacy is preserved. But at the intersection of
artificial intelligence in Genomics England, I might have
slightly different concerns that don't relate directly with
privacy. I usually think about three – scalability, automation
bias and explainability. So I mentioned before that there's a
risk of promoting issues that genomics research already faces,
that over-sampling of certain populations. So if we take what
genomics can teach us based on mostly European ancestry data, we
end up imposing assumptions on populations across the globe.
 The role of AI here is in scaling the impact of those
assumptions, taking bias algorithmic models, and applying them to
diverse communities within and beyond the UK, risks not
identifying certain conditions, missing patterns, potentially
informing poor medical practices, if we take these bias data
samples and ultimately algorithms. So the issue here about
scalability is that artificial intelligence promotes the
limitations of genomics research.   


The second issues I mentioned on automation bias is about
individuals, people potentially valuing the output of
computational systems because they're mathematical and therefore
might seem objective. And the challenge here is very real, if we
have a clinician who is diagnosing someone and the clinician says
no, there's no clear evidence for there being cancer. Following
all the metrics that this clinician has learnt over the years of
their work, and they're faced with an AI tool that says that this
agrees that says actually there is a case for there being cancer
or whatever the other option is.  So the automation bias
there, if it were to kick in, would be for the clinician to raise
their hands, give up and say, "Well, the machine says that there
is..." or "there isn't cancer, so we'll just go with what it
says."  The other option of is for the clinician to actually
challenge what the AI tool says.  And the crucial difference
here is that the rationale of the clinician can be described, it
can be outlined, explained. And that's the third issue, that's
the issue of explainability.  


So modern AI tools tend to use an enormous data set and neural
networks or other machine learning technologies where outputs are
produced with little or no explanation. The clinician can explain
why they decided on one diagnosis or another, the AI tool cannot.
Ideally, this is the really tricky bit, hospitals, Genomics
England and others, would have the government structures in place
to handle these discrepancies in outputs from clinicians who can
explain what they have to say, and AI tools which are
mathematically very sophisticated, they sound pretty cool, it's a
challenge.  


Natalie Banner: It absolutely is a challenge, and very helpful to
talk through some of those broader ethical issues and questions.
Because they are, they're questions to what I understand, you
know, the law and regulation hasn't caught up yet with these
very, very rapidly advancing tools and technologies. And
actually, if we are working at the frontier of some of these,
then these ethical questions are precisely the ones that we need
to work how to navigate through.  Not necessarily because of
a regulatory structure, but just through bringing different
voices, different perspectives to the table, trying to anticipate
consequences, and thinking through where some of those questions,
for example, as you raised on explainability, what could we do?
Where could we address some of those challenges? 


Francisco Azuaje: Yes. The issue of transparency is crucial, not
only to ensure that we have useful tools, but to ensure that we
improve privacy, that we respect the uses of these technologies.
At the same time regardless of the techniques that we use to make
systems more explainable or interpretable, the idea behind
transparency also means, let's ensure that if we say that
something works well, indeed, we are providing evidence that that
something is working well. That means that we ensure that first
of all we have reliable and robust systems, and that by doing
that we are also bringing actual benefits to patients and
society. So I think that's a more fundamental question than
discussing which techniques can make this model or that model
more explainable, or the actual practices for making something
more transparent. So in general this transparency is there
because we want to ensure that we deploy ethical, robust and fair
systems.  And that this starts by enhancing the quality and
transparency of the development of the tool, but also the
evaluation of those tools before their deployment, and even after
these systems have been deployed to a research environment or to
a clinical setting.  


Ismael Kherroubi García: It sounds like there's a need for
continuous monitoring, right. Throughout the life cycle of
developing an AI tool, but also once implemented how we get
feedback, so that the tool can be improved, but also future and
other tools can be improved. 


Natalie Banner: Thank you so much. So we have had a real whistle
stop tour through the world of AI and genomics.  We've
highlighted some real potential advances in exciting areas. We've
cautioned about some of the risks and questions about how to
tackle some of the ethical complexities that are emerging. So
just to wrap us up, Francisco, can I turn to you first, could you
tell us what you see as being the biggest or the most significant
impacts in the world of AI and genomics in the next, say, three
to give years? 


Francisco Azuaje: In the next three to five years, we should
expect significant advances in genomics, AI genomics, beyond the
focus individual genes or markers or actually the idea of gene
panels.  So we should expect that the full patient genomic
analysis will become more common to provide a more comprehensive
view of genetic influences on health, and also the combination of
genomic data with other types of health information will offer
deeper insights for supporting more accurate, faster medical
decision-making.


The challenge lies in connecting this data to clinical decisions
moving beyond diagnosis to actually recommend personalised
treatment options.  Matching patients with relevant clinical
trials based on their genomic and other types of clinical
information will also become more effective, more efficient.
However, concerns about reliability, safety of these applications
remain, and I think that in the next few years we will see an
acceleration in the development of tools and applications. But
also an improvement in the way we evaluate these tools before
they are deployed to a real-world environment.  So this will
be crucial in the next few years, and despite all these
challenges, there is reason to be very optimistic about the
future of AI in genomics and medicine for the benefit of
patients.   


Natalie Banner: Thank you, Francisco. And Ismael, a last word to
you, what's your key takeaway for those developing AI tools for
use in genomics? 


Ismael Kherroubi García: For me the biggest challenge is that
there must be multidisciplinary approaches, so those developing
these tools need to speak with one another and be exposed to
patients. So, on the one hand, AI tools for medical applications
must involve multidisciplinary collaborations, critically
including the voices of clinicians, and that point was raised by
Francisco. And the COVID-19 pandemic, to work with something...
work for an example, already showed us the value of behavioural
and other social sciences in understanding the impacts of public
health policies. So genomics and general genomics research must
consider multidisciplinarity in a similar way and bring different
disciplines together.


On the other hand, genomics data remain intricately linked with
individuals. Research participants and patients must be kept
abreast of developments in the complex space that is this
interaction of AI and genomics to avoid the trust issues
mentioned earlier on. Ultimately, those developing AI tools for
use in genomics must follow inclusive practices. 


Natalie Banner: We'll wrap up there. Thank you to our guests,
Ismael Kherroubi García and Francisco Azuaje for joining me today
as we discussed the role of AI in genomics and healthcare, and
the importance of having open, informative conversations about
both the promises and the challenges in this exciting space. If
you'd like to her more like this, please subscribe to the G Word
on your favourite podcast app. Thank you for listening, I've been
your host, Natalie Banner.