U of T and AI: Faculty members on their research and its impact

Updated: Feb 12

Original author: Simona Choise Published: October 24, 2018 Source: https://www.utoronto.ca/

More time to interact with patients, improved efficiency in health-care delivery, and new global communities of exchange and communication: According to University of Toronto researchers, these are just some of the positive outcomes of artificial intelligence.

“AI has the power to change the way we live and work, making more human capital available to focus on creativity and innovation,” says Cristina Amon, dean of the Faculty of Applied Science & Engineering.

Artificial intelligence and automation, and the impact of advanced technology on business strategy and workforce demand and supply, is one of the themes that will be discussed at this year’s Ontario Economic Summit. Together with leaders in the private, public and not-for-profit sector, U of T researchers and entrepreneurs will be showcasing how they are seizing these opportunities.

Work at U of T is already demonstrating how advances in machine learning can accelerate gains in health, education, communication and quality of life, as well as the importance of educating students to think about the ethics of the technology.

Fahad Razak, assistant professor at the Institute of Health Policy, Management and Evaluation Team member, General Medicine Inpatient Initiative (GEMINI)

What makes some patients more likely to have a poor prognosis after being discharged from hospital?

The answer lies somewhere in the three billion data points now included in the General Medicine Inpatient Initiative (GEMINI) health database, says Razak, who is also an internist at St. Michael’s Hospital.

GEMINI is the result of a team of physicians, public health advocates, and data scientists working to standardize much of the information hospitals collect about patients across seven Ontario hospitals. By tracking and comparing data on admission, discharge and tests, among other metrics, GEMINI will lead to more accurate predictions about the outcomes for individual patients and the ability to intervene to improve them.

Three billion individual pieces of data “is the kind of volume we need to develop predictions,” Razak says.

But the process of standardizing the data from different hospitals has also revealed gaps in the information now collected.