The past five years have been boom times for AI startups courting venture investors in healthcare, and the good times just keep rolling. Presently one of every four dollars chasing ROI in the sector goes to companies with AI in their wheelhouse.
And that’s a lot of dollars. This year is expected to see more than $11 billion of venture capital invested in healthcare AI—up from $7.2 billion in 2023.
The figures are from a new report by Silicon Valley Bank, which further finds AI deal activity growing faster in healthcare than in tech overall.
The report notes that much fanfare attends healthcare AI’s capabilities in drug discovery and therapeutics. However, the authors point out, most investment activity suggests expectations are no less great for AI adding value to other aims.
Here are four key observations from SVB vice president Raysa Bousleiman and co-authors as presented in an executive summary.
1. Administrative applications are fast out of the gate.
The initial wave of AI in healthcare—2019 to the present—is largely focused on streamlining administrative burdens like managing revenue cycles, establishing data interoperability and scheduling patients, SVB reports.
Administrative AI accounts for 27% of AI healthcare investment and 42% of deal volume so far in 2024, Bousleiman and co-authors show. Meanwhile, they add, clinical AI “faces more scrutiny due to regulatory hurdles and more difficult adoption, often requiring a workflow change to implement.” More:
‘For providers and payers, it’s still early days for their AI strategies. Many are targeting administrative AI first since it carries less risk and leads to clear efficiency gains.’
2. Healthcare AI startups that tout technical impressiveness over operational value will struggle.
And that’s to find not only investors but also customers. By the same token, companies that can leverage a provider’s existing infrastructure in their new product “may find preference” among venture capitalists, the authors write. For example, 70% of U.S. providers use Epic for electronic health records, and AI companies in the EHR space “may find themselves with a leg up” if they establish interoperability with the Epic system.
“It’s not enough for companies to be the ‘shiny AI object,’” the SVB experts observe. “Rather, near-term financial validation and access to quality input data are crucial.” More:
‘While AI deal activity is relatively resilient compared to the wider ecosystem, investors and buyers are critically evaluating new AI solutions by determining how readily they’ll be adopted and how much business value they truly offer.’
3. Startups’ flexibility is their strength.
Organizations often favor established players when purchasing AI solutions, the authors underscore. To compete in a crowded landscape, successful startups “clearly articulate why they’re the better choice compared to larger competitors that organizations may already have as partners.”
“Massive adoption and margins are not as crucial for startups as they are for established big tech,” the authors point out. “Also, the bottom-up nature of startups makes them suitable for working closely with physicians.” More:
‘In order to access the necessary data, AI startups often find it beneficial to co-develop features with their customers.’
4. Startups selling AI-enabled diagnostic tests may strain to make the juice worth the squeeze.
Challenges in this category abound, SVB notes, and the troubles range from navigating regulatory hoops to managing high costs and securing quality data.
“For startups, being nimble is important for finding alternative paths, such as risk sharing, monetization and profitability,” Bousleiman and co-authors write. “Demonstrating the cost savings for payers may help companies get the revenue to justify costs until payers broaden their perspective on paying for diagnostics.” More:
‘We believe the short-term path forward is for companies to understand who is writing the check and how payers define value.’
Preview or download the report here.
