Paige.ai (an acronym for Pathology AI Guidance Engine) is a New-York based startup that was founded earlier this year. The main aim of the company is to use artificial intelligence (AI) to better understand cancer pathology and develop better cancer detection methods.
Thanks to a generous $25 million raised in funding and a sound deal with the Memorial Sloan Kettering Cancer, Paige.ai is all set to make headlines as it continues to develop and grow its AI technologies. First the company is going to concentrate on prostate, breast , and other major cancers, but has plans to grow as quickly as possible.
The funding was led by Breyer Capital. “Paige.ai is poised to become a powerhouse in computational pathology and an undisputed leader among thousands of healthcare AI competitors,” said Breyer. “Today, we take a major step forward in harnessing machine learning and more fully recognizing its promise for cancer diagnosis and treatment.”
Co-founders of the startup are Dr Thomas Fuchs, and Dr David Klimstra. Together they intend to focus on using AI in cancer pathology in order to devise better ways of diagnosing cancer and develop more effective treatments also. A lot of the techniques used throughout pathology today were devised more than 100 years ago, so it’s about time we had a change.
“Patients deserve and need an accurate diagnosis as quickly as possible, yet our current methods are time-consuming, expensive and subjective,” stated Klimstra. “The field is ripe for innovation and we are confident that Paige.ai will aid pathologists in detecting disease better and faster. With computational pathology, pathologists can redirect their efforts toward more sophisticated tasks, such as integrating histologic findings with other diagnostic analyses.”
There are really two tasks Paige.ai is trying to complete. One is to soak up loads of data in order to sufficiently train the AI platform, while the other is to use existing researchers to actually teach the system some of their own techniques and ways of doing things. It’s similar to that of autonomous cars, when really they are only going to be useful if they can learn to think like a human.
Fuch belives that compiutational patholgy is the missing piece of the puzzle when it comes to diagnosing cancer. “We will enable computational pathology to expand at the scale necessary to achieve intelligent, quantitative clinical models – and facilitate widespread adoption of digital pathology,” he said.