Three Companies Rocking AI In Medicine

AI is having amazing effects on the world of medicine. It's helping us to more capably diagnose patients. It's providing assistance with radiology imaging. It's working with pathogens and microbes and very small things.

In terms of the scope of AI in medicine, it’s helping us with things at all scales, from a molecular level, all the way up to a patient care level.

So let's look at a few companies that are making inroads in changing how we view healthcare.

All three of these companies were represented in a panel at the IIA event this spring. We also had Ava Amini, senior researcher at Microsoft, who added her own insights to thoughts on enterprise use of AI in this sector.

So let's look at what each of these firms is doing in this space:

GNS

GNS is pursuing advances in biomedicine. Some of it is around new drugs – figuring out how patients will respond, for example.

We heard from CEO and cofounder Colin Hill about how this is working out.

Hill pointed out that 80% of drugs in clinical trials fail. GNS is trying to change that, while also working on DNA research, where AI has changed the game for the human genome.

By the way, you can check out this link to see an interview with Hill where he makes statements like this:

“Causal machine learning is a powerful form of artificial intelligence that has the ability to not just find patterns in data, which is what a lot of traditional methods such as deep learning do, but to use the data as fuel to reconstruct the underlying mechanisms of the system that created the data in the first place. With these underlying mechanisms unraveled, it then enables “what if?” interventions such as a one drug versus another to be run on the computer to determine the optimal treatment for an individual patient. This is key is solving the matching problem and getting the right treatment to the right patient at the right time versus treating patients as if they were some hypothetical ‘average patient.’ This is critical not just to curing diseases and slowing disease progression, it is also critical to savings hundreds of billions of dollars in interventions not matched to the right patient and the downstream medical cost of prolonged disease.”

OpenEvidence

Then there's Zachary Ziegler from OpenEvidence, who is associated with places like Harvard and Cornell, and is working on important use cases around medical AI.

OpenEvidence was built, in the words of its spokespersons “to aggregate, synthesize and visualize clinically relevant evidence in understandable accessible formats that can be used to make more evidence-based decisions and improve patient outcomes.”

That’s a lot!

The company has pioneered a system for clinical assistance for doctors, and this model can bring a lot of the current information to where it's needed in order to diagnose, medicate and more.

Stability AI

Tanishq Mathew Abraham was also with us to talk about what they're doing at Stability AI, where researchers are making advances, for example, in the radiology space.

Open foundation models, he said, are fundamental to bringing this type of innovation.

The panelists also talked about why now is the time that so much is happening with AI in the medical world.

Some suggested there was a shift in biology, where we now look at the ability to generate insights without a concrete or predetermined hypothesis.

“The rules have changed,” Ziegler said, also talking about the alignment of three things: scale of model, scale of compute, and scale of data. “As a community, we haven't even begun to scrape 1% of what we can do with this glorious world that we live in.”

Those are some of the critical things that these three companies are doing, and they’re just some of the many disruptors that are working their magic in a field that's always been science-based, but only now has begun to be highly technologized in this way.

I want to continue to bring these kinds of insights here, and highlight a lot of what's going on as we see it up close with our panelists and speakers, and all of the participation that we see from people and institutions.