Venture capital has poured nearly $18 billion into healthcare AI in a single year. The return on that investment, by most accounts, is a smorgasbord of pilots. Roughly 83% of health systems are running generative AI experiments—and only 5 to 10% of those ever reach enterprise-wide deployment. The rest die in procurement, drown in integration reviews, or launch in two departments and quietly stop expanding.
UCSF Health, Kleiner Perkins, and Doerr Capital don’t think that’s purely a technology problem. They’re betting that the technology is being built in the wrong place.
The trio, Fortune learned exclusively, is launching UCSF Health Converge, a health AI accelerator built around a deliberately radical constraint: two to three companies a year, embedded inside UCSF’s actual clinical workflows, IT systems, and operational teams from day one. UCSF Health CEO Suresh Gunasekaran is blunt about what the program is pushing back against. “The vast majority of solutions, when they exit the accelerator and come to pitch us, are not ready to implement at the system level,” he told Fortune. “That final mile is missing.”
It’s a direct shot at an industry that has spent the better part of a decade building healthcare AI from the outside looking in. And UCSF isn’t the first to try a different approach. Mayo Clinic has run more than 70 startups through an accelerator program since 2022 that gives companies access to its patient data and expert feedback in exchange for a stake in the company. Cleveland Clinic struck a deal with Khosla Ventures last fall that lets the firm’s portfolio companies bring their products in and test them on real patients and providers. UCSF’s argument is that testing a product inside a hospital is fundamentally different from building it there.
