Healthcare AI Trends to Watch: M&A Exits & Generative AI Use Cases

Healthcare AI Trends to Watch: M&A Exits & Generative AI Use Cases
Healthcare AI Trends to Watch: M&A Exits & Generative AI Use Cases


Artificial intelligence certainly isn’t new to the digital health field, but the sector seems to be in the midst of a new hype cycle. This one is focused on novel technologies like large language models and other forms of generative AI, said Alex Lennox-Miller, lead analyst at CB Insights, during a Thursday webinar.

The healthcare AI space will be an exciting one to watch over the next couple years, as investment dollars flow to startups and providers and payers launch more AI pilots. Two of the most interesting trends to watch will be the use cases that providers and payers will prioritize when deploying generative AI models, as well as M&A activity within the healthcare AI field, Lennox-Miller declared.

There are some areas of healthcare, like image analysis and digital pathology, that have been benefiting from AI for years now, he pointed out. But as the healthcare sector begins to focus more on novel applications for AI, especially generative AI, one of the use cases that is getting the most attention is automated documentation and ambient notetaking for providers.

Some companies building generative AI models for clinical documentation include Nuance (acquired by Microsoft), Abridge, DeepScribe and Suki. It’s important to note that while this area is garnering attention, the industry’s efforts surrounding the use of generative AI for clinical documentation are still quite nascent, Lennox-Miller said.

“I think we’ve seen some pilots that are very promising, but this is a technology area that we’ve seen people trying to solve for quite a while. While it’s interesting to see the potential applications of generative AI here — and there are some companies that have really interesting, effective real-time products — we’re also seeing a few companies that still need a lot of work on their transcripts, documentation and manual corrections to produce really usable documentation and notes,” he explained.

At least for now, Lennox-Miller thinks that the use of generative AI in healthcare will remain mainly on the “nonclinical side of things.” He expects to see providers deploy these AI models to improve patient engagement, establish informational chatbots, summarize patient histories for better pre-visit planning, and send patients home with detailed plans for follow-up care. Generative AI also holds significant potential to automate referral letters and prior authorization requests, he added.

Lennox-Miller pointed out that five of the 10 biggest digital health funding rounds in the second quarter of this year were raised by AI companies. HeartFlow raked in $215 million, and Strive Health snagged $166 million. Additionally, Spring Health, BenchSci and Flywheel all closed financing rounds totaling more than $50 million.

With the exception of Strive Health’s Series C fundraise, all these companies’ financing rounds took place at the Series D stage or later. When it comes time for later-stage healthcare AI companies like these to make an exit, Lennox-Miller predicted that most will be bought instead of filing an initial public offering.

“We’re seeing a lot of consolidation in the space — a lot of acquisitions by larger companies that are looking to add functionality to an existing platform. This is almost reaching a point where our projections are hitting nearly the level of M&A that we saw in 2021, at the peak of AI enthusiasm. But there has really only been one significant healthcare AI IPO, which was from Bullfrog AI in drug development. So while the space is getting a lot of investment, we’re also seeing a lot of consolidation and a lot of acquisition,” he explained.

In Lennox-Miller’s view, AI is an area of technology in which large incumbents have significant advantages over startups. Tech giants like Microsoft, Google and AWS have a lot more resources and computing capacity, enabling them to effectively handle the vast volumes of data needed to develop a strong AI model, he said.

“Frankly, AI is mostly a matter of iteration. The more you can do it and the more time you can take to refine and build it before it has to become a profitable product, the more effective it will be. So M&A really represents these larger organizations that have those natural advantages in the AI space taking advantage of what they see on the market,” Lennox-Miller declared.

Photo: metamorworks, Getty Images



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