Program Requirements
The M.Sc. in Experimental Medicine; Digital Health Innovation focuses on the basics of clinical epidemiology, medical artificial intelligence, clinical innovation, and applied data science, including the use and generation of digitized health and social data using specialized software. Fundamentals of current AI applications in medicine, methods to employ big data in clinical tool development, mathematical principals underpinning digital health and big data, and design thinking methodology in clinical innovation. High-volume streams of clinical and health-related data from clinical systems, wearables and social media.
Thesis Courses (24 credits)
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EXMD 693 Master's Thesis Research 4 (12 credits)
Overview
Experimental Medicine : Independent research work under the direction of the Thesis Supervisor and the Supervisory Committee.
Terms: Fall 2024, Winter 2025
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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EXMD 694 Master's Thesis Research 5 (12 credits)
Overview
Experimental Medicine : Independent research work under the direction of the Thesis Supervisor and the Supervisory Committee.
Terms: Fall 2024, Winter 2025
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Required Courses (9 credits)
- EXMD 601 Real World Applications of Data Science and Informatics (3 credits)
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EXMD 634 Quantitative Research Methods (3 credits)
Overview
Experimental Medicine : Topics covered include: 1) An overview of common research designs based on examples from research currently undertaken in the Division of Experimental Medicine; 2) Types of data arising from these designs; 3) Basic methods for data analysis; and 4) Application of these methods to student research projects.
Terms: Fall 2024
Instructors: Dendukuri, Nandini (Fall)
Restriction: Must be registered for graduate or postdoctoral studies in the Faculty of Medicine or the Faculty of Science.
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EXSU 500 Artificial Intelligence in Medicine
(3 credits)
Overview
Experimental Surgery : Introduction to artificial intelligence (AI) applied to issues in medical diagnosis, therapy selection and learning from health data. Various AI methods, electronic medical records, and ethical/security concerns. Machine learning approaches including deep learning and reinforcement learning without delving too deeply into the technical details.
Terms: Fall 2024
Instructors: Hooshiar, Amir; Fevens, Thomas; Barralet, Jake (Fall)
Complementary Course (6 credits)
3 credits from the following:
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EPIB 600 Clinical Epidemiology (3 credits)
Overview
Epidemiology & Biostatistics : Principles and methods of epidemiology, as applied to clinical practice and clinical research. Key principles of testing and measurement in the clinical context, as well as study design, analysis, and inference in the clinical research setting.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Course offered during the Summer Session only.
Restriction: Restricted to McGill Medical Residents and Clinical Fellows or permission of the instructor for other clinicians.
Due to the intensive nature of this course, the standard add/drop and withdrawal deadlines do not apply. Add/drop is the third lecture day and withdrawal is the sixth lecture day.
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EXMD 600 Principles of Clinical Research (3 credits)
Overview
Experimental Medicine : Foundations for conducting clinical research including the principles underlying clinical studies, an overview of key methods in clinical research and the critical interpretation of peer-reviewed literature.
Terms: Fall 2024
Instructors: Rahme, Elham; Almeida, Nisha; Sewitch, Maida; Grover, Steven; Menzies, Richard Ian; McDonald, Emily; Fishman, Jennifer; Sebastiani, Giada; Fortier, Isabelle; Sapir-Pichhadze, Ruth (Fall)
There are no set prerequisites, but it is expected that the student will have some background in mathematics; understanding functions and basic algebra is essential.
3 credits from the following:
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EXMD 630 Developing Digital Innovations for Health Impact (3 credits)
Overview
Experimental Medicine : Advanced innovative thinking, knowledge and skills that will aid in the development of innovative digital health solutions. It will integrate mind mapping strategies, design thinking, and usability evaluations that form the core of developing and refining an innovative digital health solution aimed to solve a well-defined clinical/public health problem.
Terms: Winter 2025
Instructors: Pai, Nitika (Winter)
Restriction: Permission of the instructor
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EXSU 620 Surgical Innovation 1 (3 credits)
Overview
Experimental Surgery : The process of surgical innovation and acquisition of hands-on skills necessary to work within a multi-disciplinary team in the creation of a novel, need driven, and marketable prototype used in the care of the surgical patient. This is the first of a 3 part course introducing concepts and performing needs analyses.
Terms: Fall 2024
Instructors: Barralet, Jake; Mwale, Fackson (Fall)
Corequisite(s): EXSU 619
Prerequisite(s): Permission of instructors.
Restriction(s): Course requires entry to surgical theatre; this is subject to hospital approval.
1) Students may be subject to interview.
2) Contact hours 46.5 , 31.5 hours lectures, 15 hours workshops and hospital visits.
3) Language of instruction: English, French available.
4) Minimum number 6, maximum 30
5) Subject to completion of medical requirements/immunization record.
6) Professional conduct and dress required at all times in hospital visits when in potential contact with patients.
Elective Courses (6 credits)
6 credits of courses at the 500 level or higher approved by the Director.
