Education Overview
Highly trained individuals are needed to operate complex diagnostic tools. This is why Artificial Intelligence (AI) education in medicine is a priority for T-CAIREM.
We envision a comprehensive approach to education that includes a speaker series featuring leading experts, training and teaching from practitioners, workshops, hackathons and student rounds, symposia, and collaborative specialization and certificate programs.
At the very core of our education program are two practicums each year that pair students with data partners and T-CAIREM faculty to solve real-world problems and obtain effective results.
T-CAIREM’s education activities will be available to trainees of different backgrounds and levels of expertise in order to create collaborative, multidisciplinary learning opportunities.
We’re looking forward to working with the next generation of medical practitioners. The innovations our members and future students develop will lead to more efficient treatment methods, lower health costs and create better health outcomes for Canadians.
Computational and AI in Medicine at LMP
• Prospective students interested in finding an AI in health supervisor can use this faculty directory to search for "Artificial Intelligence" under the "Research Interests" tab: https://lmp.utoronto.ca/finding-supervisor-your-msc-or-phd
• The University of Toronto's Department of Laboratory Medicine and Pathobiology currently offers two courses in computational medicine. (More courses and programs will be available soon.)
Dr. Rahul Krishnan
Topics in Machine learning - Machine Learning for Healthcare
(Course: CSC2541)
This course provides LMP students with a broad overview of the application of machine learning to solve healthcare problems. Students will learn about the recent successes of graphical models, deep learning, time series analysis, and transfer learning in a healthcare environment.
Dr. Bo Wang
Basic Principles of Machine Learning in Biomedical Research
(Course LMP1210H)
This course is intended for University of Toronto graduate students in Health Sciences to learn the basic principles of machine learning in biomedical research and to build and strengthen their computational skills in medical research.