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Ontario researchers use AI to diagnose and treat COVID-19

CIFAR and the Ontario Government support major breakthroughs in COVID-19 research.

Three Ontario research teams are using AI to identify novel solutions for understanding, diagnosing, and treating COVID-19. 

The Ontario Government is providing funding with more than $200,000 in support of innovative new research underway by CIFAR researchers across the province.

“Ontario is home to some of the brightest researchers in the world,” says Ross Ramano, Ontario Minister of Colleges and Universities. “Through our Government’s COVID-19 Rapid Research Fund we supported world leading efforts to prevent, detect and treat COVID-19. We are proud to have supported the research at CIFAR; research which has made a demonstrable difference in our collective fight against COVID-19.”

The grants enable multidisciplinary research teams to come together with the goal of advancing our knowledge of the virus, develop new treatments, and understand how it is spreading through the population.

“The CIFAR AI and COVID-19 grants are mobilizing researchers to find creative solutions for the pandemic,” says Dr. Alan Bernstein, President & CEO, CIFAR. “We are thankful for this generous support from the Ontario Government so that our AI researchers have had the freedom and flexibility to explore high-risk, high-reward ideas. This investment is paying off in real solutions that support the health of Ontarians and people around the world.”

Using AI to Identify COVID-19 Treatments: Led by CIFAR Azrieli Global Scholar Jean-Philippe Julien (SickKids), this project is using AI to identify existing, safe drugs that can be repurposed to treat COVID-19 patients. Working with Canadian startup, Cyclica Inc., the team used AI to virtually screen over 10,000 drugs, narrowing down the list to 15 priority candidates. The team has identified at least one approved drug to date, with more possible drugs that can be repurposed for treating COVID-19 underway.

Tracking the Latest COVID-19 Research with AI: A team led by CIFAR Associate Fellow Kyunghyun Cho (New York University) and Jimmy Lin (University of Waterloo) developed an open-source information system called Neural Covidex, which indexes and summarizes relevant biomedical literature related to COVID-19. This allows biomedical researchers, medical professionals, as well as scientists from other disciplines, and policy makers to stay up-to-date on the latest COVID-19 research. Since its launch, the platform has been used by 4,000 unique visitors from around the world each month, and is constantly updated to index the latest batch of scientific articles on COVID-19, as well as clinical trial data.

COVID-19 Diagnostic X-rays: Canada CIFAR AI Chair Marzyeh Ghassemi (Vector Institute, University of Toronto) is co-leading a team of scientists using AI tools to predict the severity of pneumonia in COVID-19 patient x-rays.Though not yet approved for medical use, the tool has proven significantly accurate, and unlike previous AI-driven diagnostics, it uses x-rays rather than radiation, which is more expensive and less available than CT scans. The team is working to expand the testing cohort and clinical evaluation.

The Ontario projects are among fourteen projects supported by CIFAR AI and COVID-19 Catalyst Grants, which provide funding for innovative, high-risk, high-reward ideas to address the COVID-19 pandemic. CIFAR’s AI and COVID-19 Catalyst Grants Program is funded by the Ontario Government, Microsoft through the AI for health program, the Natural Sciences and Engineering Research Council of Canada (NSERC), Genome Canada, the Max Bell Foundation and many individual donors. Launched in March 2020, the program funds innovative, high-risk, high-reward ideas and projects that address the COVID-19 pandemic.  The projects explore a range of topics using AI and are at varying stages of progress.   


AI and COVID-19 Catalyst Grant recipients:

  • PanXcea: PANdemic Prediction with X-ray-based COVID-19 External Analysis
    Collaborators: Marzyeh Ghassemi (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Joseph Paul Cohen (Mila, Université de Montréal), Chris McIntosh (University of Toronto)
  • COVIDEX – Advanced Information Retrieval for Clinical and Academic Literatures
    Collaborators: Kyunghyun Cho (CIFAR fellow,  Learning in Machines & Brains program, New York University),  Jimmy Lin (Vector Institute, University of Waterloo)
  • Leveraging Biomedical Knowledge Graphs for COVID-19 Drug Repurposing Strategies
    Collaborators: Jian Tang (Canada CIFAR AI Chair, Mila, HEC Montréal), William L. Hamilton (Canada CIFAR AI Chair, Mila, McGill University), Yoshua Bengio (Canada CIFAR AI Chair and co-director, CIFAR Learning in Machines & Brains program, Mila, Université de Montréal), Guy Wolf (Mila, Université de Montréal), Yue Li (Mila, McGill University)
  • COVID-Net: An Open Source Deep Learning Platform for COVID-19 Detection and Risk Stratification
    Collaborators: Alexander Wong (University of Waterloo), James Lee, Linda Wang, and Desmond Lin (University of Waterloo)
  • Rapid, Automated Assembly of SARS-CoV-2 Phylogenies
    Collaborators: Quaid Morris (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Jeffrey Wintersinger (University of Toronto), Jeff Wrana and Ben Blencowe
  • Modeling the Transmission of SARS-CoV-2 Between Zoonotic Sources on a Gene Level
    Collaborators: Guillaume Rabusseau (Canada CIFAR AI Chair, Mila, Université de Montréal), Vladimir Makarenkov (Université du Québec à Montréal), Bogdan Mazoure (Mila, McGill University)
  • MyTrace / MaTrace: A Privacy-Compliant Contact-Tracing Mobile App for COVID-19
    Collaborators: Alán Aspuru-Guzik (Canada CIFAR AI Chair,  Lebovic Fellow, Bio-Inspired Solar Energy program, Vector Institute), Maryzeh Ghassemi (Canada CIFAR AI Chair, Vector Institute, University of Toronto)
  • Machine Learning Against COVID-19: Accelerating Small Molecule Drug Discovery
    Collaborators: Sarath Chandar (Canada CIFAR AI Chair, Mila, Polytechnique Montréal), Matthew Taylor (Amii, University of Alberta), Sai Krishna (99andBeyond), Karam Thomas (99andBeyond)
  • Guarding At-Risk Demographics with AI (GuARD-AI)
    Collaborators: Daniel C. Baumgart (University of Alberta), Martha White (Canada CIFAR AI Chair, University of Alberta, Amii), Randy Goebel (Amii, University of Alberta), Geoffrey Rockwell (Kule Institute for Advanced Study, University of Alberta), Robert Hayward (Chief Medical Information Officer, Alberta Health Services), Shy Amlani, (Virtual Health), Jonathan Choy (Virtual Health), Sara Webster (Virtual Health), and Sarah Hall (Virtual Health)
  • Detecting and Monitoring Pneumonia in COVID-19 Patients Using Machine Learning and Ultrasound Imaging
    Collaborators: Kumaradevan Punithakumar (University of Alberta), Russell Greiner (University of Alberta, Amii), Jacob Jaremko (University of Alberta), Nathaniel Meuser-Herr (Upstate Health Care Center, NY), Dornoosh Zonoobi (
  • AI-Driven Identification and Validation of Drug Repurposing Candidates to Treat COVID-19
    Collaborators: Jean-Philippe Julien (CIFAR fellow, Molecular Architecture of Life program, University of Toronto), Costin Antonescu (Ryerson University), Cyclica Inc. (industry partner), Phoenox Pharma (industry partner)
  • Preventing COVID-19 Infection in Families: The COVID-19 Child and Family Study
    Collaborators: Jonathon Maguire (Hospital for Sick Children), Anna Goldenberg (Canada CIFAR AI Chair and Lebovic Fellow, Child and Brain Development program, Vector Institute, University of Toronto, Hospital for Sick Children), Marzyeh Ghassemi (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Catherine Birken (Hospital for Sick Children), Peter Jüni (St. Michael’s Hospital) Kevin Thorpe (Sunnybrook Hospital), Charles Keown-Stoneman (St. Michael’s Hospital), Mary Aglipay (St. Michael’s Hospital)
  • Tracking Mental Health During the Coronavirus Pandemic
    Collaborators: Alona Fyshe (Canada CIFAR AI Chair, CIFAR Learning in Machines & Brains program, Amii, University of Alberta), Daniel Lizotte (Western University), Rumi Chunara (New York University)
  • Planning as Inference in Epidemiological Dynamics Models
    Collaborators: Frank Wood (Canada CIFAR AI Chair, Mila, University of British Columbia), Benjamin Bloem-Reddy, Alexandre Bouchard, Trevor Campbell (University of British Columbia)

Learn more about CIFAR’s action on COVID-19.

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