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<v Narrator>2024 is going to be</v>

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an exciting one for generative AI.

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<v ->My name is Ashkan Afkhami.</v>

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I'm a managing director and partner,

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and I look after our healthcare practice

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from a digital analytics perspective.

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And I'll be moderating today's panel.

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People are getting ready for an exciting 2024.

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What are you most excited about in 2024 on this topic?

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<v ->Last year was all about understanding generative AI.</v>

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I think we've done a great job collectively as an industry

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understanding the potential opportunities for generative AI.

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<v ->The area that I'm particularly excited</v>

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is large vision models and multimodal model,

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because healthcare is predominantly multimodal images.

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How do we combine the EMR data

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with the medical imaging data,

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with the data that's coming from monitoring

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in meaningful way?

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That's where GenAI can really help streamline the data

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and then different persona users can query the same data

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to get meaningful insights.

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<v ->And there is massive opportunity</v>

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when you see all the data in biology

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to use the technology of GenAI.

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Once you have enough data,

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you can, from the data, train models

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that capture information in the data.

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<v Narrator>As companies become more familiar with GenAI,</v>

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they're learning how to prioritize use cases.

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The first use cases are focusing on automation.

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<v ->We'll start with operational efficiency.</v>

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How do we improve better bedside prediction,

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streamlining the scheduling, the EMR,

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reducing the product development cycle?

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There's a real ROI where a lot of things

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which could have taken five to 10 years

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can actually be done in one year.

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<v ->First, a particular single use case, start with security.</v>

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Because ultimately, if you can work

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with your security teams to improve security,

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then you're actually going to build a solid foundation

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for how you build services on top of that-

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your data platform, your AI platform,

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your experience platforms.

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<v Narrator>GenAI can also play an important role</v>

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in improving the customer experience.

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<v ->We do truly believe that generative AI</v>

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can actually reinvent customer engagement.

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Can we now use technology to augment that experience

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to make it better, richer?

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Can we move down the intelligent self-service route

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so patients or members can actually

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find out more information?

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And then on the other side, inside of the organization,

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can we equip the people in the organization

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to operate at the top of their license

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so they can spend more time with the patient

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rather than actually looking at the screen?

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<v Narrator>Healthcare companies are facing</v>

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a second major challenge.

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How do you scale GenAI use cases successfully?

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<v ->There are probably four areas that I'd kind of highlight.</v>

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The first one is digital skills,

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making sure we're providing appropriate readiness

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at all layers of the organization

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to understand the opportunity,

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but also the risks and limitations.

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The second then is really having a well-defined approach

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for use case ideation.

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And so every use case that you will ideate on

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is validated in the same way.

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The third area is really taking a platform approach

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to the technology adoption.

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Making sure you're building an appropriate platform

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with the appropriate guardrails

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and the appropriate governance

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and organizational frameworks in place

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so you can actually scale fast once you're ready to go

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and you've approved those use cases.

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And then the fourth is the importance of partnerships.

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<v ->The way we develop AI solutions</v>

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for drug discovery, et cetera,

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is that we need expertise and we need data.

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For our strategy to access the data,

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it's great to partner with hospitals, medical centers,

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and this is crucial because you need good data

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in order to train the model.

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Partnering with hospitals requires

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a long-term relationship, trust building, and technology

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to make sure that the data is not leaked,

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that there is no personal information leaking.

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<v ->It's the importance of building these connections</v>

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because ultimately, there's

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not going to be a single frontier model

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or a open source model that's going to be able

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to solve all of the challenges in healthcare.

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You do need partners across the data, the domain,

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as well as partners, both in the ISV world,

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partners that actually sit in front of those data estates,

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as well as partners that actually do the implementation.

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<v ->We have been heavily investing around building</v>

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what's called a medical imaging foundation models.

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So rather than having a smaller model,

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if you scale it and then bring it down,

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it actually gives you more accuracy.

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That smaller model we can even embed

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into our ultrasound devices.

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As scale is important,

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as with traditional AI and machine learning,

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data is equally important.

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It's possible that your data size might decrease,

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but you need to ensure the quality of the data,

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the governance around that is there.

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It also helps you reduce the cost.

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<v ->There's a huge opportunity collectively as an industry</v>

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for us to start talking about

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where are we seeing value return

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and can we replicate that for the benefit of society

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rather than the benefit of our own individual organizations?

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