In their own words, students in the Digital Health Innovation program share their perspectives and experiences.
Gregory D. Gooding, 2021 cohort
"The Experimental Medicine Digital Health Innovation concentration has allowed me to explore intersections of medicine and science that I have never experienced before, which has led to a profound impact on my academic career. Having a variety of professors and guest speakers share their expertise in their fields (psychology, medicine, artificial intelligence, data science) by giving concrete and real-world examples was the cherry on top of an already engaging curriculum.
Arguably, the most important skill I’ve acquired is learning how to collaborate within multi-disciplinary teams. I had the opportunity to be guided through the innovation process and create solutions with students from a variety of backgrounds, such as computer sciences, physics, business, engineering, and nutritional sciences. Having everyone put in a bit of their background added layers of complexity and perspective that enriched our projects and led to eye-opening conversations.
One of the most unique aspects of the program was being able to synthesize big data and clinical findings to create innovative medical solutions applicable to real-world problems, from conception to implementation. The program has equipped me with knowledge and tools that will make a difference in my future endeavours."
Leslie Unger, 2021 Cohort
Harry Moroz, 2021 Cohort
"While completing a bachelor’s degree in Mathematics and Computer Science at McGill, I gained a special interest in how AI and Data Science can solve real world problems. The intersection of machine learning, technology and medicine became a specific passion of mine. The Experimental Medicine MSc concentration in Digital Health Innovation offers an innovative focus towards the application of newly developed digital solutions in the medical world. The field of digital health innovation, specifically the application of AI methods to clinical settings, is fairly new. Having the opportunity to learn from field experts and physicians who conduct and implement cutting-edge research made my decision to apply very easy. This program offers the opportunity to shape my career as an innovator and AI researcher, as well as to gain relevant clinical insight.
The first year of this program involves a variety of courses spanning from hands on innovation courses, clinical statistics, and data analytics. Going into these courses having limited prior knowledge, I was worried I’d be behind. However, the program and its instructors take into account the diverse backgrounds of their students and offer many opportunities for help. I was able to not only excel in the curriculum, but gain relevant knowledge and experience which I have already utilized in my research and developing a start-up innovation. In addition to the required coursework, I have been fortunate enough to be supervised by Dr. Ariane Marelli, whose research on Congenital Heart Disease is perfectly in line with my goals for research. Our research aims to apply deep learning methods to model the electronic health records of patients with heart failure in order to help predict future health outcomes and disease trajectories. My co-supervisor Dr. Yue Li from the Department of Computer Science and Dr. Marelli’s combined experience in deep learning, biomedical data and cardiology has allowed me to understand how to apply complex machine learning algorithms to real clinical problems.
From e-Health and m-Health solutions, to building online applications to aid care in third world countries, it is truly inspiring how McGill researchers are leveraging technology to improve healthcare systems and patient care. I believe that my experience working closely with doctors and my knowledge of new digital solutions has positioned me to be at the forefront of the rapidly evolving field of digital health innovation. I plan to seek more experience in the medical setting by applying to medical school upon finishing my Master’s degree, while also continuing my research of applying machine learning concepts to solve medical needs."