AI is having an incredible impact on the world of medicine. This helps in better diagnosing the patient. We provide support for radiological imaging diagnosis. It acts on pathogens and microorganisms and very small things.
When it comes to the scope of AI in healthcare, AI is assisting us at every scale, from the molecular level to the patient care level.
So let's take a look at some companies that are actively working to change the way we view healthcare.
All three companies appeared on panels at this spring's IIA event. We also asked Ava Amini, a senior researcher at Microsoft, to add some unique insight to his thoughts on how companies can use AI in this space.
So let's take a look at what each of these companies is doing in this space.
GNS
GNS pursues biomedical advances. Some of this is about new drugs, such as understanding how patients respond.
We spoke to CEO and co-founder Colin Hill to find out how this works.
Hill pointed out that 80% of drugs in clinical trials fail. GNS is trying to change that by working on DNA research, where AI has transformed the human genome landscape.
By the way, if you check out this link you can watch an interview where Hill says the following:
“Causal machine learning is a powerful form of artificial intelligence that does more than just find patterns in data, as many traditional methods such as deep learning do, it uses that data as fuel to It has the ability to reconstruct the underlying mechanisms that created the data in the first place. Once these underlying mechanisms are unraveled, the “what if” is possible. Interventions such as running one drug against another on a computer are used to determine the best treatment for an individual patient. This is about solving matching problems and getting the right patient the right treatment at the right time, rather than treating them as if they were a hypothetical “average patient.” This is important not only to cure the disease and slow the progression of the disease, but also to save hundreds of billions of dollars in interventions that are not matched to the right patient and downstream medical costs from prolonged illness. . ”
open evidence
Next, OpenEvidence's Zachary Ziegler is working with Harvard University, Cornell University, and others on important use cases for medical AI.
In the words of a spokesperson, OpenEvidence provides “clinically relevant evidence that can be used to make more evidence-based decisions and improve patient outcomes, in an easy-to-understand and accessible format. Built to aggregate, synthesize, and visualize.
That's a lot!
The company is a pioneer in clinical support systems for doctors, and this model can deliver a lot of up-to-date information where it's needed for diagnosis, treatment, and more.
Stability AI
Tanishq Mathew Abraham also joined us to talk about the work researchers are doing with Stability AI, which is making progress in the field of radiology, for example.
He said the open infrastructure model is the basis for bringing about this type of innovation.
The panelists also talked about why there is so much happening in the medical world with AI right now.
Some have suggested that there has been a shift in biology to focus on the ability to generate insights without specific or predetermined hypotheses.
“The rules have changed,” Ziegler said, also talking about adjusting three factors: model scale, computational scale, and data scale. “As a community, he hasn't even begun to shave off one percent of what we can do in this wonderful world we live in.”
These are just some of the important things these three companies are doing, and are just a few of the many disruptors working their magic in a field that has always been science-based, but Only now is it starting to become highly technological. Method.
I hope to continue to provide insights like this here, and to bring more of what's going on into focus, with panelists, speakers, and up-close participation from people and institutions. I am.