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The º£½ÇÉçÇø College of Health Sciences is launching an AI + Health Hub to harness the power of data and computing science to solve a wide range of health-care challenges from quicker diagnoses to streamlining hospital queues and discovering new drugs.

The new hub brings together more than 120 researchers from 10 faculties to collaborate, educate and innovate, with the goal of turning AI-driven health-care research into solutions that are accessible to all Albertans.

“It’s an exciting opportunity to bridge the digital divide so we can use new technologies to improve health care for everyone,” says hub co-lead , a nephrologist and vice-dean of clinical research in the Faculty of Medicine & Dentistry.

“We are building a community around the way artificial intelligence is designed and deployed in health-care settings,” says co-lead Gillian Lemermeyer, assistant professor in the Faculty of Nursing and assistant adjunct professor in the John Dossetor Health Ethics Centre. 

“We want to build on partnerships that already exist between computer scientists and engineers and physicians, and expand that to include all health-care disciplines, artists and sociologists and students and industry,” Lemermeyer says. 

The º£½ÇÉçÇø is already a global leader in artificial intelligence, earning $100 million for AI research since 2017, with 24 based at the university and a strong relationship with the (Amii), the only institute of its kind in Western Canada.

Alberta is also home to the largest population-based health data set in North America, which allows research to be based on information about the health of people of all ages, ethnicities and demographic backgrounds, says Pannu, noting that Denmark and Taiwan are the only other places in the world with such a rich resource.

“We have access to the smartest AI scientists in the world and an integrated health system with a single electronic health record for the province, so when we analyze data and discover improvements, we can design tools that benefit everyone and deploy them at scale,” Pannu says.

“If you’re developing a diagnostic algorithm in a veterans’ hospital in the U.S., for example, or in a population of people with private health insurance, the demographic composition is so different from the general population that the product or tool you develop may not be effective when used for an underrepresented population,” Pannu explains. “We