(soft music)
Narrator: 2024 is going to be
an exciting one for generative AI.
-: My name is Ashkan Afkhami.
I'm a managing director and partner,
and I look after our healthcare practice
from a digital analytics perspective.
And I'll be moderating today's panel.
People are getting ready for an exciting 2024.
What are you most excited about in 2024 on this topic?
-: Last year was all about understanding generative AI.
I think we've done a great job collectively as an industry
understanding the potential opportunities for generative AI.
-: The area that I'm particularly excited
is large vision models and multimodal model,
because healthcare is predominantly multimodal images.
How do we combine the EMR data
with the medical imaging data,
with the data that's coming from monitoring
in meaningful way?
That's where GenAI can really help streamline the data
and then different persona users can query the same data
to get meaningful insights.
-: And there is massive opportunity
when you see all the data in biology
to use the technology of GenAI.
Once you have enough data,
you can, from the data, train models
that capture information in the data.
Narrator: As companies become more familiar with GenAI,
they're learning how to prioritize use cases.
The first use cases are focusing on automation.
-: We'll start with operational efficiency.
How do we improve better bedside prediction,
streamlining the scheduling, the EMR,
reducing the product development cycle?
There's a real ROI where a lot of things
which could have taken five to 10 years
can actually be done in one year.
-: First, a particular single use case, start with security.
Because ultimately, if you can work
with your security teams to improve security,
then you're actually going to build a solid foundation
for how you build services on top of that-
your data platform, your AI platform,
your experience platforms.
Narrator: GenAI can also play an important role
in improving the customer experience.
-: We do truly believe that generative AI
can actually reinvent customer engagement.
Can we now use technology to augment that experience
to make it better, richer?
Can we move down the intelligent self-service route
so patients or members can actually
find out more information?
And then on the other side, inside of the organization,
can we equip the people in the organization
to operate at the top of their license
so they can spend more time with the patient
rather than actually looking at the screen?
Narrator: Healthcare companies are facing
a second major challenge.
How do you scale GenAI use cases successfully?
-: There are probably four areas that I'd kind of highlight.
The first one is digital skills,
making sure we're providing appropriate readiness
at all layers of the organization
to understand the opportunity,
but also the risks and limitations.
The second then is really having a well-defined approach
for use case ideation.
And so every use case that you will ideate on
is validated in the same way.
The third area is really taking a platform approach
to the technology adoption.
Making sure you're building an appropriate platform
with the appropriate guardrails
and the appropriate governance
and organizational frameworks in place
so you can actually scale fast once you're ready to go
and you've approved those use cases.
And then the fourth is the importance of partnerships.
-: The way we develop AI solutions
for drug discovery, et cetera,
is that we need expertise and we need data.
For our strategy to access the data,
it's great to partner with hospitals, medical centers,
and this is crucial because you need good data
in order to train the model.
Partnering with hospitals requires
a long-term relationship, trust building, and technology
to make sure that the data is not leaked,
that there is no personal information leaking.
-: It's the importance of building these connections
because ultimately, there's
not going to be a single frontier model
or a open source model that's going to be able
to solve all of the challenges in healthcare.
You do need partners across the data, the domain,
as well as partners, both in the ISV world,
partners that actually sit in front of those data estates,
as well as partners that actually do the implementation.
-: We have been heavily investing around building
what's called a medical imaging foundation models.
So rather than having a smaller model,
if you scale it and then bring it down,
it actually gives you more accuracy.
That smaller model we can even embed
into our ultrasound devices.
As scale is important,
as with traditional AI and machine learning,
data is equally important.
It's possible that your data size might decrease,
but you need to ensure the quality of the data,
the governance around that is there.
It also helps you reduce the cost.
-: There's a huge opportunity collectively as an industry
for us to start talking about
where are we seeing value return
and can we replicate that for the benefit of society
rather than the benefit of our own individual organizations?
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