U-M bets that the right match is where health AI innovation begins
By Eric Shaw
Finding the right collaborator at a research university shouldn’t take years of conference hallways and cold emails. A new University of Michigan program is trying to compress that process to weeks, then fund what the newly matched teams build.
Developing tailored therapeutics requires two kinds of expertise that rarely share a building: the clinical researcher who understands the disease and the computational specialist who knows how to model it. Before the science can start, those two people have to find each other. That’s what this program is designed to make happen.
The AI MedTech Match Grand Challenge, launched in December, pairs clinical investigators at Michigan Medicine with AI specialists from across the U-M campus to develop early-stage research proposals. The program provides up to a year of financial support, enough runway for teams to move a promising idea to a point where it can compete for external funding.
The inaugural competition is being organized through a partnership among AI & Digital Health Innovation, the Rogel Cancer Center and the Department of Surgery, which together contributed $100,000 in total funding to be distributed across two winning teams.
Here’s how the challenge works: Clinical faculty with research ideas submit short video pitches describing a problem they want to solve. AI specialists across campus review those pitches and flag the projects they’d most like to join. The clinical faculty then select their collaborator. Once paired, the teams develop written proposals, which are evaluated by experts from Michigan Medicine, the College of Engineering and the School of Public Health. The strongest proposals advance to a pitch event on April 11.
“The University of Michigan is home to some of the greatest AI and clinical expertise, yet it can be challenging for these experts to find each other,” said Megan Haymart, associate director of data solutions at AI&DHI and professor of internal medicine at Michigan Medicine. “The goal of AI MedTech Match is to facilitate those collaborations so that the researchers can focus on what they do best.”
The inaugural competition has drawn 11 applications, suggesting the unmet demand was real. The first cycle focuses on cancer and surgery; the two funded teams will work in each of those areas. The program was inspired in part by the Grand Challenge model developed by the Max Harry Weil Institute for Critical Care Research and Innovation, adapted here for the specific challenge of bridging clinical and computational expertise.
AI&DHI plans to expand the program to additional clinical areas and is actively seeking partners to support future iterations. Whether the funded projects ultimately produce the kinds of tools that change patient care remains the work ahead. That’s what the funding period is for. But the structural piece U-M is betting on is the match itself: that the right pairing, given enough support and time, is where therapeutic innovation begins.
“It’s only the first year, and yet we’re thrilled with the number of matches,” said Jenna Wiens, co-director of AI&DHI and associate professor of computer science and engineering. “There’s no shortage of interesting problems, and faculty in the College of Engineering are excited to work with our colleagues in Medicine.”