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Pearl, the healthcare spinout from LA-based AI startup, GumGum, raises $11 million

GumGum, the Los Angeles-based startup that’s spent the past decade applying machine learning technologies to advertising and sports, has spun out a new healthcare startup focused on the dental industry called Pearl. The company has raised $11 million in financing from undisclosed strategic investors and Craft Ventures, the investment firm set up by former Yammer […]

GumGum, the Los Angeles-based startup that’s spent the past decade applying machine learning technologies to advertising and sports, has spun out a new healthcare startup focused on the dental industry called Pearl.

The company has raised $11 million in financing from undisclosed strategic investors and Craft Ventures, the investment firm set up by former Yammer founder, David Sacks.

GumGum’s co-founder, Ophir Tanz, stepped down from the adtech giant to run the new startup last month, while GumGum’s president and chief operating officer, Phil Schraeder took the reins as chief executive at GumGum.

“This idea was seeded within GumGum,” says Tanz. “I started the process of collecting dental x-rays over three years ago.”

GumGum’s strategy has been to build out a holding company of computer vision driven businesses, Tanz says. Both its portfolio of services for advertising and for sports franchises have become profitable on their own, and the opportunity in healthcare was too tempting of a target to pass up.

For Tanz, the decision to set up Pearl as a separate business was necessary for the new company to be able to focus on a huge opportunity to transform a portion of the healthcare industry that has remained largely untouched by machine learning applications.

It’s also a space that’s ripe for technology to come in and give a more clear-eyed assessment of patient health than the industry standard currently provides.

“We are isolated from the larger health-care system. So when evidence-based policies are being made, dentistry is often left out of the equation,” Jane Gillette, a dentist in Bozeman, Montana, who works closely with the American Dental Association’s Center for Evidence-Based Dentistry, told “The Atlantic” recently. “We’re kind of behind the times, but increasingly we are trying to move the needle forward.”

Pearl may be one way to move that needle.

It’s also a return to the family business, for Tanz, whose father worked as a dentist for decades.

“The thing with dentistry is that it’s always somehow the forgotten medicine, but it’s such a massive market opportunity,” says Tanz. 

Machine learning in the dental business can achieve four main objectives, says Tanz. It can reduce fraud for insurers, validate the performance of dentists in networks that are being created through the consolidation of small practices by large private equity firms, and automate workflows inside the dental office.

Imagine having diagnostics tools integrated with medical devices through software that can be distributed and updated remotely, giving practitioners the best quality information. That’s the goal for Pearl, Tanz says.

Eventually, the company will look to expand to other verticals within healthcare, but for now, the new money is focused on building out its toolkit for teeth.

“We’ll expand beyond dental eventually,” says Tanz. “We’re going to be focused on dentistry and the dental category and the laboratory for quite a while.”

The company is coming to market with three products: “Second Opinion”, which scans x-rays and identifies pathologies and anatomy to ensure a proper diagnosis; “Practice Intelligence”, which delivers advanced analytics for dental practices and groups to deal with patients more effectively; and “Smart Margin”, which provides feedback on intraoral scans for dental restoration and manufacturers.

“Pearl will have an immediate positive impact on the dental category,” said Tanz, “It will streamline tedious, repetitive tasks, enhance profitability across dentistry, and, most importantly, it will improve the standard of care by validating diagnoses, removing large elements of uncertainty from the dental equation.”

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