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There are various iterations of a lighthearted meme going around LinkedIn these days, poking fun at CEOs who raise their hands and declare that they want more AI, they want it now—but they’re not quite sure what for.
While it’s clearly in jest, there’s some truth to these memes. In the “Gold Rush” era of AI adoption over the last few years, leaders don’t want to be left behind, but in many cases, they haven’t quite identified specific challenges that AI can help them solve. It’s an unfortunate truth that a lot of AI use cases seem more about “AI for AI’s sake” and less focused on strategic deployment.
In our recent co-sponsored webinar, “Applying AI to Real-World Health Data,” our panelists spent some time exploring the idea that we’re now entering a “second wave” of AI adoption. In this article, we’ll expand on this conversation, which featured great insights from Unite Genomics CEO Taner Dagdelen; Vivek Mukhatyar, Generative AI Medical Engagement Lead at Pfizer; Lance Hill, CEO at Within 3; and moderator Paulo Machado, CEO at Health Innovation Inc.
When generative AI first came out, there was a lot of money invested in and a lot of focus paid to pilot programs–with healthcare leaders attempting to simply wrap their arms around this new technology and its potential.
As Lance Hill describes in the webinar, “When generative AI first came out, no one really knew what it was, but we all just knew it was something we should all be focused on.”
Some early examples of first wave AI solutions in healthcare included the use of public AI agents that pharma personnel could use to boost work productivity, without sharing confidential information (e.g., “I have a presentation with senior leadership on topic ABC. What questions should I expect?”)
During this first wave, while there was a lot of excitement about AI’s potential, it was paired with some apprehension about the impact the new technology would have on the healthcare job market.
“I think there were a lot of people who thought right away that AI was going to very quickly replace scores of jobs in life sciences and I think that didn’t pan out and I don’t think will pan out for quite a while,” says Lance. “I think there were folks that thought that early gen AI would rapidly break down silos between pharma and the rest of the ecosystem. I think some of the more grandiose ideas that are going to change the fundamental ecosystem have not happened yet, as they require understanding large amounts of information.”
As healthcare and tech leaders have abandoned the first wave initiatives that haven’t bore fruit, a new era of AI begins. This second wave is more complex, but also much more strategic, more focused, and more impactful. After casting such a wide net in the first wave, healthcare industry leaders are starting to hone in on the use cases where they can see real, measurable upsides.
“Now that we’re in the second phase, we’re realizing, ‘no, no, no,’ these are the places where there’s potentially value based on where the tech is,” says Lance. “We’re starting to see the beginning of real impact and ROI that will scale over the next few years.”
In this new era, we’ll see more custom solutions, as well as more secure solutions that allow users to share more information with the model while still adhering to organizational data management policies—while driving meaningful productivity increases.
To continue with the analogy from the first wave—asking a generative AI tool to generate likely questions for a senior leadership presentation—in the second wave, the output opportunities are exponentially greater and more helpful. For example, gen AI tools can help develop your slide deck, source relevant patient and HCP interviews and other proof points, and take notes in real time during the presentation.
Administrative-type use cases are just the tip of the iceberg when it comes to AI use cases in healthcare during this exciting second wave. As AI solutions get more deeply and broadly embedded into healthcare systems, there are myriad use cases in everything from clinical decision support to improving quality of care. Here’s just a high-impact use cases we’re seeing so far:
As healthcare institutions continue to prioritize use cases that directly impact patient outcomes and organizational efficiency, there can be an impulse to try and predict what’s around the corner on the AI front: will there be a third wave? When? And what enhancements and capabilities are likely to be rolled out?
Unite Genomics CEO Taner Dagdelen cautions against this kind of prognostication, instead pausing for a moment to reflect on how far the industry has come.
“One thing we all have to internalize is that the future is actually now,” he says. “The last sector that I would expect to go full bore into adopting AI technology and deploying it on top of their data would be healthcare organizations. Historically, they are so slow to adopt things…but shockingly, they were some of the first to adopt AI. And it’s not just pilots–it’s full-scale deployments.
This rapid adoption means that providers, care teams, and patients are already experiencing the effects of AI on their healthcare experiences today, and it’s a promising sign for what may be around the corner.
“The fundamental dynamics in healthcare around how decisions are made has changed,” says Taner. “Patients are much more part of the decision-making circle than they were before, because information is so readily available and also it’s digestible–in layman’s terms–in a way that’s never been possible before. It’s much more of a dialogue between patients and physicians when they’re making decisions about what care steps need to be taken next.”
“The pace of change has definitely accelerated across healthcare,” says Paulo Machado. It will be very interesting to see how quickly pharma can keep up with health systems, insurance companies, and other stakeholders.”
