AI Investor Forum: Gus Tai on AI in Healthcare (Part 4)

Sramana Mitra: Yes. On the process side, we’ve had a company become quite successful in the program. It is bootstrapped. We looked at raising money, but eventually, it was going fine as a bootstrapped company. I think it continues to bootstrap. It’s called CliniOps.

They did a lot of the infrastructure of clinical trials. They’ve managed a lot of international trials. So, I think your point is well taken is that there is opportunity to be cost effective by doing these trials elsewhere in different parts of the world.

Those of you who are listening and have expertise in this domain, you could develop expertise in how to bring in data from less expensive geographies.

Now, then comes the rest of the distribution chain. We are talking about diagnostics and how to get the technology of diagnostics into the systems and how to get them approved and so forth.

There’s also the distribution of care – whether it’s diagnostic care or treatment. Telehealth is starting to make some inroads internationally. The US healthcare system seems to be offering telehealth as well. How do we think about that?

There are several things that go on a regular basis that should be automated, right? For example, as people age, there’re more medications and conditions to be managed. There’re more medications that they’re taking on a regular basis. Those medications interfere with each other. There’re side effects – one triggering another, and so on and so forth.

This kind of stuff seems ideal for AI applications, and there should be telehealth systems that operate on this data and proactively guide patients. What are the possibilities of this kind of behavior happening? And, when you apply patient level personalization and medication data, this is a rich application.

Gus Tai: Yes, absolutely. I know of a venture firm specializing in companies in this particular area. It’s Alpha Edison in Southern California.

Given this richness and complexity of individuals, how do you personalize what is the right type of medication or nutrition for that person while also imbuing that intelligence with general biology of human beings, which is still poorly understood. How do you apply the interactions that take place for the generic person, which isn’t well understood, to the generic customization or personalization for the individual?

So, both levels matter. I think that as we roll out AI, we’ll see increasing sophistication of personalization. I would say that it could even be bootstrapped and done simply initially. Suppose you’re a functional health company or selling some sort of nutraceuticals, which aren’t regulated to the same degree in the US as drugs, those businesses have good cash flow characteristics. If you can find a niche of consumers who find the personalization and what you’re serving of value, then the gross margins on that business are high enough to help you grow. You could grow for a while and go from there.

I think that could be interesting for your audience or the community around how we use AI to incrementally improve personalization and have a foundation for that. I view this as a form of value capture. You’re doing value creation by meeting needs, but then how do you capture that value? In nutraceuticals, you capture it by selling consumables that are renewable and have high gross margins.

This segment is part 4 in the series : AI Investor Forum: Gus Tai on AI in Healthcare
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