University of Toronto
Search this site
Menu

Artificial Intelligence

The power of computing to transform diagnostic tools, identify personalized treatments and better understand population health are now evident. To be an international leader in training, practice and research, U of T Medicine must harness the incredible potential of this technology and the expertise present in the region. This is why the Strategic Academic Plan outlined the following goals:

  • Build capacity to reflect the emerging role of artificial intelligence in health professions.

  • Build training opportunities around AI will raise awareness and reduce stress and anxiety about the use of AI in clinical care.

  • Technology is at the heart of advancing this goal. In developing strategic partnerships that will advance this goal, we will ensure clear policies are in place for material transfer agreements as well as data ownership, management and security.

2020 Highlights: 

  • Established the Centre for AI Research and Education in Medicine (CAIREM) as an EDU-C to be a focal-point for research, education and infrastructure in this field.

  • Appointed Muhammad Mamdani as the Inaugural Director of CAIREM.

  • Hosted the Machine Learning in Medicine Symposium, which included a keynote address by Isaac Kohane, the inaugural Chair of the Department of Biomedical Informatics at Harvard Medical School.

Muhammad Mamdani and Vinyas Harish

Muhammad Mamdani [left] – in addition to be a professor in the Leslie Dan Faculty of Pharmacy, the Faculty of Medicine, and the Dalla Lana Faculty of Public Health – is also Vice President of Data Science and Advanced Analytics at Unity Health Toronto. He is the inaugural director of the Centre for AI Research and Educational in Medicine (CAIREM). Vinyas Harish [right] is an MD/PhD student who co-founded the Artificial Intelligence in Medicine Student Society (AiMSS).

Q: What kinds of projects are you excited about tackling at the Centre?

Mamdani: The hope for the Centre is to advance artificial intelligence in medicine. The way we’ve approached this is quite collaborative and multi-disciplinary. So, we have three themes – there’s a theme in education, a theme in research and a theme in infrastructure. The neat part about this is that we fully recognize that advancing artificial intelligence in medicine is a team sport, so we’re bringing together experts from both inside and outside the Faculty of Medicine, in areas like computer science, statistics, engineering and medicine.

Harish: Felipe Morgado and I started a student group in the Faculty of Medicine in 2017-2018 focusing on artificial intelligence, during our first year in med school. We’ve tried to think of what kinds of hands-on experiences we could foster for students, such as sharing research opportunities. I think med students are always keen for hands-on learning and putting things that they’re learning into practice. As a learner co-lead at the Centre, my hope is that we can connect students who are interested in breaking into this space with supervisors who can guide them. It’s really exciting that we now have access to all the resources of the centre to make some really exciting stuff happen.

Q: What are some practical outcomes that artificial intelligence can achieve in medicine and health care, like, for example, in relation to the COVID-19 pandemic?

Harish: For my PhD, I am thinking about how COVID-19 has put immense stress on health systems. I’m interested in digital epidemiology. I’d like to examine how we can use data that’s collected outside the health system to help us deliver better care, using methods like machine learning. These methods can help build a more resilient system.

Mamdani: Artificial intelligence shines when it has a lot of information to learn on. In a pandemic situation, with COVID-19, we haven’t seen this before. What I think is important is focusing on real-time data and what we can do in the short term with the information that’s available. So, we have to be nimble, and use that information to make decisions. I think real time data is especially important in acute situations like pandemics.

Health care is lagging when it comes the digital revolution and artificial intelligence, in comparison with other sectors. But it’s coming. There will be dramatic and transformative changes, and that’s why we’re creating the Centre.

– Gabrielle Giroday