Inside a red-bricked building on the north side of Washington DC, internist Shantanu Nundy rushes from one examining room to the next, trying to see all 30 patients on his schedule. Most days, five of them will need to follow up with some kind of specialist. And odds are they never will. Year-long waits, hundred-mile drives, and huge out of pocket costs mean 90 percent of America’s most needy citizens can’t follow through on a specialist referral from their primary care doc.

But Nundy’s patients are different. They have access to something most people don’t: a digital braintrust of more than 6,000 doctors, with expert insights neatly collected, curated, and delivered back to Nundy through an artificial intelligence platform. The online system, known as the Human Diagnosis Project, allows primary care doctors to plug into a collective medical superintelligence, helping them order tests or prescribe medications they’d otherwise have to outsource. Which means most of the time, Nundy’s patients wait days, not months, to get answers and get on with their lives.

In the not-too-distant future, that could be the standard of care for all 30 million people currently uninsured or on Medicaid. On Thursday, Human Dx announced a partnership with seven of the country’s top medical institutions to scale up the project, aiming to recruit 100,000 specialists—and their expert assessments—in the next five years. Their goal: close the specialty care gap for 3 million Americans by 2022.

In January, a single mom in her 30s came to see Nundy about pain and joint stiffness in her hands. It had gotten so bad that she had to stop working as a housekeeper, and she was growing desperate. When Nundy pulled up her chart, he realized she had seen another doctor at his clinic a few months prior who referred her to a specialist. But once the patient realized she’d have to pay a few hundred dollars out of pocket for the visit, she didn’t go. Instead, she tried get on a wait list at the public hospital, where she couldn’t navigate the paperwork—English wasn’t her first language.

Now, back where she started, Nundy examined the patient’s hands, which were angrily inflamed. He thought it was probably rheumatoid arthritis, but because the standard treatment can be pretty toxic, he was hesitant to prescribe drugs on his own. So he opened up the Human Dx portal and created a new case description: “35F with pain and joint stiffness in L/R hands x 6 months, suspected AR.” Then he uploaded a picture of her hands and sent out the query.

Within a few hours a few rheumatologists had weighed in, and by the next day they’d confirmed his diagnosis. They’d even suggested a few follow-up tests just to be sure and advice about a course of treatment. “I wouldn’t have had the expertise or confidence to be able to do that on my own,” he says.

Nundy joined Human Dx in 2015, after founder Jayanth Komarneni recruited him to pilot the platform’s core technologies. But the goal was always to go big. Komarneni likens the network to Wikipedia and Linux, but instead of contributors donating encyclopedia entries or code, they donate medical expertise. When a primary care doc gets a perplexing patient, they describe their background, medical history, and presenting symptoms—maybe adding an image of an X-ray, a photo of a rash, or an audio recording of lung sounds. Human Dx’s natural language processing algorithms will mine each case entry for keywords to funnel it to specialists who can create a list of likely diagnoses and recommend treatment.