While artificial intelligence now guides everything from transportation to our music choices, the promise of harnessing data to inform and transform our health has more often been a matter of hope than reality.
This may soon change as Western launches Ontario's first interdisciplinary graduate field dedicated to machine learning in health and medicine.
"Machine learning has really been a big buzzword in recent years but people are now realizing it's not just hype. I think everyone in health, medicine and biomedical research needs to recognize that this is going to be an essential tool," said Ali Khan, director of the new specialization and a professor of medical biophysics and imaging at the Schulich School of Medicine & Dentistry.
The new collaborative specialization in machine learning in health and biomedical science will draw together as many as 30 master's and doctoral students who already have degrees in computer science, health, engineering or medicine.
Together, they'll become conversant - even if not completely fluent - in each other's professional languages.
They'll learn to apply machine learning to solve massively complex health issues that couldn't be solved any other way.
Some applications could include reprogramming surgical robots, creating tools that identify early dementia through speech patterns, and data-crunching algorithms that can predict and prevent mental illness.
Ali Khan
In labs where Khan works, it could mean new discoveries in neuroscience, for example. "There's a huge amount of imaging data that just isn't captured in any systemic way. Imaging is often used for individual assessments but there's a lot of data that can be used to determine how biological systems are functioning or search for subtle abnormalities we didn't know existed."
Collaboration among data scientists and health scientists is crucial, said computer science and statistics professor Jörn Diedrichsen, a co-developer and facilitator of the program.
"Data is the new oil in health and biomedical science," said Diedrichsen, who is also researches the neuroscience of movement through Western's Brain and Mind Institute. "This will revolutionize health care in the next 10 years."
But, too often, neither side of the equation knows the challenges the other faces - or even what questions to ask or what problems need solving.
Jörn Diedrichsen
"The big question that this collaborative specialization is addressing is, how do we train people to actually bridge this gap? They won't become experts at each other's specialties but the goal is to give them enough background to intelligently use those techniques and talk with each other about machine learning and health," he said.
Within the interdisciplinary field, students will be learning far more than the technical aspects of machine learning and health science.
They'll be discovering how to conduct enriched research, how to examine and explore the ethical and privacy challenges, how to work across disciplines, and how to bring discoveries from lab bench to hospital bedside.
Significantly, this is the only specialization of its kind accredited by the Vector Institute for Artificial Intelligence, the federally funded non-profit group intended to grow a stronger AI sector across Canada.
Public and private health industry employers linked with Vector AI are also in need of researchers, engineers and scientists who have this expertise, Khan said.
"There's a lot of value added in having these collaborations and in having connections with Vector," he said.
Through Vector, as many as four one-year scholarships will be offered to master's level students, Diedrichsen said.
The new Western specialization is expected to start in September 2022.
Diedrichsen noted there's already a strong foundation for this in collaborations that have already formed between and among faculty, in five newly appointed professors in bioinformatics, and in a machine-learning club that's formed at Schulich Medicine.
The new specialization will be drawing upon the expertise of approximately 30 faculty members.
The collaborative specialization in machine learning in health and biomedical sciences is one of 14 Western collaborative specializations: graduate fields of study that provide additional multidisciplinary experience for students enrolled in an approved master's and/or PhD programs.
