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Upcoming Events:
Dr. Leigh Conroy

Dr. Leigh Conroy

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Meeting ID: 980 1048 6363

Passcode: 147952

Event Contact:

Dr. Qiyin Fang

The Department of Engineering Physics is holding Seminar Series, where we have invited various speakers from alumni, current students, or external industry.


Dr. Leigh Conroy, 
Medical Physicist, Princess Margaret Cancer Centre
Assistant Professor, Department of Radiation Oncology, University of Toronto
Clinician Scientist, Techna Institute, University Health Network

Application of Artificial Intelligence in Radiation Oncology – Challenges and Opportunities.


The Seminar will summarize the current landscape of machine learning and artificial intelligence applied to radiation oncology and highlight the challenges in safely using machine learning algorithms in medicine. An overview of radiation oncology and the role of medical physicists in radiation therapy and machine learning will also be provided. Dr. Conroy will also present on the career path that led her to clinical Medical Physics.


Dr. Leigh Conroy graduated from McMaster University with a B Eng Mgmt in Engineering Physics in 2010. She then completed her MSc in Medical Biophysics at the University of Toronto in 2012 and PhD in Radiation Oncology Physics at the University of Calgary in 2017. Following graduate school, she completed a medical physics residency at Princess Margaret Cancer Centre, became a Board-Certified Medical Physicist (CCPM – Radiation Therapy Physics) in 2020, and is currently a Medical Physicist in the Radiation Medicine Program, Princess Margaret Cancer Centre. As an Assistant Professor in the Department of Radiation Oncology, University of Toronto and a Clinician Scientist at the Techna Institute, University Health Network, she is active in teaching in the residency academic programs in the department of Radiation Oncology. Dr. Conroy’s clinical interests include quality management, error prevention, automation, and motion management strategies. Her most recent research focuses on the clinical implementation of machine learning in Radiation Oncology and development of quality assurance (QA) processes and education for safe use of machine learning in medicine.