Artificial Intelligence in Healthcare: Master of Science in Applied Computing (MScAC)
The Master of Science in Applied Computing (MScAC) is a 16-month professional master’s program offered by the Department of Computer Science in the Faculty of Arts and Science.
The Department of Laboratory Medicine and Pathobiology (LMP) has developed a concentration for this MScAC in partnership with the Department of Computer Science.
In the Artificial Intelligence in Healthcare concentration, you will complete courses in:
- Data science
- Artificial intelligence
- Visualization/systems/software engineering
- Approved LMP or Master of Health Informatics (MHI) courses
- Communication for Computer Scientists
- Technical Entrepreneurship
As well as an eight-month research internship.
Concentration Director for AI in Healthcare
- Professor, Departments of Laboratory Medicine and Pathobiology and Computer Science
- Varma Family Chair in Biomedical Informatics and Artificial Intelligence (AI), The Hospital for Sick Children (SickKids) Research Institute
- CIFAR AI Chair, Vector Institute
- Research theme co-lead, Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM)
Why Masters in AI in Healthcare?
Artificial Intelligence (AI) in Healthcare is an application of AI methods to the very impactful data collected in the field of healthcare and life science research.
This program fulfills the need that we anticipate will continue to grow as Artificial Intelligence (AI) tools become embedded in healthcare, specifically in patient care. This is the first truly interdisciplinary program between the faculties of Arts and Science and Medicine that will help trainees to gain the knowledge needed to lead and succeed in development and deployment of AI tools in healthcare.
The Royal College of Physicians and Surgeons of Canada and the College of Family Physicians of Canada have identified AI and Healthcare as important training areas. Such training is critical to successfully meet the goals set in the reports provided by the task forces established by these organizations and others (e.g. CIFAR) to achieve the benefits of incorporating AI into the Canadian Healthcare system and beyond.
Who is the program for?
This program with an AI in Healthcare concentration is ideal if you:
- Aim to be a healthcare provider or life science researcher with knowledge of AI, either in healthcare or biomedical research; or
- Aim to be a computer scientist or engineer who works with healthcare data
- Wish to be a driver of the development and deployment of AI into clinical practice or research.
By completing the Master of Science in Applied Computing with an AI in Healthcare concentration, you will be able to:
- Demonstrate competency in data science and machine learning for healthcare or life science research representing core foundational knowledge including programming, software, statistical analyses, machine learning modeling, data storage and retrieval.
- Understand the current technologies used to generate and analyze healthcare data
- Develop complex analysis workflows, perform and interpret statistical tests, assess biases in the data and in the model in application to healthcare datasets.
- Use basic software engineering principles such as design, scripting, prototyping, interoperability to analyze healthcare data.
- Effectively apply analytical skills and training in a real-world setting, collaborate with team members, and present employers and advisors with reproducible, critically evaluated results ready for interpretation and action.
- Use simulations and benchmark datasets to perform quality assessment on newly developed and existing machine learning approaches.
- Critically evaluate, manipulate, analyze, document and present complex healthcare/life science data and outcomes of modeling learning using existing Machine Learning tools, with some tool-development skills paired with the visualization and assessment tools.
- Communicate results to a diverse audience.
- Independently assess the latest research developments in AI and develop skills to update existing workflows when improvements arise.
- Be able to critically assess any Machine Learning application to healthcare data and the outcomes.
How to apply
Applications are open October - December.
Opening up a new world of machine learning for biomedical students: Course Director Dr. Bo Wang and his students talk about the graduate course, Basic Principles of Machine Learning in Biomedical Research