How to Secure Investment as a Healthcare AI Innovator

For both small startups and R&D departments within larger companies, making investment funds work for an AI-based product is not just about securing initial funding but about making sure the investor understands the challenges of the healthcare sector. Chad Ehrenkranz talks to physician-executive and startup advisor Rick Abramson, MD, about the challenges.

Chad Ehrenkranz: We’ve all seen that AI has enormous potential in many areas of healthcare, from the use of AI-based predictive analytics to analyze large data sets to uses in the block-and-tackle operations of healthcare administration. Rick, you have great experience in developing a piece of AI technology that takes on the role of a radiologist and in successfully bringing it to market in this space. What have you found are the challenges at the moment for innovators in securing the funding backup that they need?

Rick Abramson: I’m definitely seeing some funding challenges as the healthcare AI industry consolidates and matures and as M&A takes place. But in general, healthcare AI is currently riding a renewed wave of access to capital compared to this time last year. I’m seeing that innovators who are able to tell a convincing story about an AI-based healthcare product will find many investors out there who are willing to take a risk on an idea.

Chad Ehrenkranz: We at Sidley have seen some investors moving very quickly into the healthcare space. But we’ve also seen that investors who are not very experienced in that space are having to learn very quickly that healthcare AI is quite different from general AI, particularly in how tightly regulated it is.

Rick Abramson: Yes, many healthcare AI investors will need to climb a steep learning curve about regulatory approval, reimbursement, and procurement. An investor may also be surprised by how difficult it can be for a startup to prove a return on investment over acceptable business time horizons. I’ve seen healthcare AI startups fail to transition to a larger, established entity because they are unable to bring their investment partner on board with the particular challenges of the sector.

Chad Ehrenkranz: Of course, an investor experienced in the healthcare space will appreciate that the regulators do take a different approach to generative AI (GenAI) tools from which they take to narrower applications that relate to a particular use case.

Rick Abramson: Yes, it’s those narrower AI applications, those that perform a strictly defined set of tasks — for example, analyzing chest X-rays to determine whether signs of pneumonia are present — that are easier to get past the regulators. Although a narrow-use application may be highly complex from a computing standpoint, it is much easier to satisfy the regulators about the safety and effectiveness of the technology because the application is so focused. Whereas it can be difficult for a regulator to determine the safety and effectiveness of, for example, a GenAI healthcare chatbot, which has a much wider range of possible responses. This means that the narrow-use AI healthcare models may have greater appeal to investors with a lower risk tolerance.

Chad Ehrenkranz: That’s interesting because it’s also the narrow-use healthcare AI applications, which are of the most use for drug discovery and diagnostics, and it is here that we at Sidley expect to see investment continue to rise. We’re also seeing that the investors who are either healthcare sector-focused or technology-focused have legitimate concerns about the risks associated with GenAI. That’s even though there are some excellent applications for GenAI within healthcare, for example, a chatbot that can simulate a triage nurse or check on patients post-surgery.

Rick Abramson: Ultimately, whether you’re working with GenAI or narrow-use AI, the ideal achievement for AI healthcare innovators is to find an application where there’s an opportunity for both efficiency improvement and quality improvement. That is the desirable spot for both entrepreneurs and investors in this space.

Rick Abramson, MD, is a U.S. board-certified radiologist and former global chief medical officer for Annalise-AI, an Australia-based startup/scaleup that develops AI software to help with the interpretation of medical images such as X-rays and CT scans.

This post is as of the posting date stated above. Sidley Austin LLP assumes no duty to update this post or post about any subsequent developments having a bearing on this post.