With Canadas health care system in such disarray, it might be time to call in the machines. Currently, there are more than 300 homegrown startups working on health innovations fuelled by artificial intelligence, from smartphone tools that judge the severity of a wound to a handheld digital device that detects cardiac disease to a platform that predicts the global spread of viruses like COVID and monkeypox. While most of these systems arent ready for rollout quite yetthough in some Toronto hospitals, AI is already flagging at-risk patients who may require a transfer to the ICUtheyre poised to make a big impact in health care soon.To better understand what this future could look like, we spoke with Azra Dhalla, the Director of Health AI Implementation at Torontos Vector Institute for Artificial Intelligence. AI has tremendous capabilities, but its transformative powers have yet to be fully realized in health, and thats what were trying to change, she says. So were working with hospitals, health care agencies and academia to take this world-class research and translate it into something thats really tangible. Here, Dhalla discusses AIs potential to bring hospital wait times way down, the need for more diverse health data and why a robot probably wont be the one tinkering with your wonky knee.
Before we get to the future of health care, are there places where AI is already being used in medicine?
Yes. Theres ChartWatch, which focuses on predictive analytics. Its an early-warning system that pulls vitals from patients in the internal-medicine ward to predict whether a transfer to the ICU or a death will occur. So the predictive power of these solutions can really lead to improved decision-making and the ability to intervene early.
What about other AI models working away in the background?
There are some, but I would say that in health care, very few models have been deployed in a clinical setting. Health care has a number of challenges, especially when were dealing with datawe have to be very stringent about security, about privacy, about confidentiality. The thing with AI algorithms is that, similar to how you and I learn, AI algorithms get better as more data is provided. But that can only happen if we can actually get access to it, which is very difficult in health care. However, weve partnered with Gemini, a data collaborative of more than 30 hospitals data in Ontario, the largest of its kind in Canada, and thats allowed Vector researchers to develop cutting-edge AI models and solutions, including studies related to COVID-19.
What are some of those projects in development right now?
Id say there are three areas worth highlighting: personalized medicine, drug discovery and creating a more efficient health system. With personalized medicine, algorithms can help us predict illness and support patients long-term. So, for example, you can use AI to predict Alzheimers disease based on changes to speech patterns. Or you could use it to discover insights within imaging data that can guide treatment and therapy decisions for patients with breast cancer. With drug discovery, AI can analyze pharmacological and health data to find different combinations of drugs that can be used to target existing and emerging viruses, or treat conditions that the drug might not have originally been prescribed for. And with health systems, it could help alleviate wait times faced by patients in hospitals, which is a big issue in Canada right now. When you bring in AI, the potential for us to better allocate resources, both in terms of staffing and funding, is fantastic and leads to better patient outcomes.
On the other hand, what isnt going to happen with AI and health care? You must hear some pretty wild theories when people find out what you do.
One thing is that AI is not going to replace a physicianit will augment clinical decision-making, but it wont replace it. Its more like a virtual second opinion, not meant to override human judgement or expertise.
So Im not going to roll up for knee surgery and find a robot about to perform it on me?
WellI cant predict the future. But I dont think thats going to happen.
What do you hear from doctors and health care practitioners when you talk to them about AI?
They really do want to know how were using AI to revolutionize health care, and they want to know not just on a theoretical level but a practical level. How can they use these solutions in a clinical setting? What does it mean for patient care overall? Thats always their number-one questionwell, actually, Ill say there are two questions. Number one, will it be disruptive to my workflow? And the second is, what are the outcomes that can be produced for a patient?
What worries them about their workflow?
What they say is: We dont want another button to press. We want it to be very seamless. And also they worry whether this all happens in a black box. Explainability in AI is very importantwe dont want to just use this blindly. So if an algorithm makes some kind of decision, we need to know how it has actually come up with that decision.
We hear a lot about bias in AI. How can bias skew an algorithms results?
You hear the expression garbage in, garbage out. AI algorithms will always reinforce bias if the data theyre trained on is biased. If were looking at a pool of health care data that is only representative of a certain segment of the population
Say, white men of a certain age?
Thats right. Then when you try to apply the AI model to a different or a more diverse population segment, it doesnt work, or it wont work in the same way. A good example is an image-recognition model that wasnt able to recognize melanoma in patients with different skin types, because the model wasnt trained on data that was representative of the whole population. I will say that theres much work being done on responsible AI, making sure that we correct for inherent biases.
And how do we do that?
By ensuring that theres access to very diverse data. And then by looking at that data to really say when it isnt representative of an entire population, so that if there are inherent biases, we can correct that at the forefront. We also want to make sure our models work for everyone. So in AI implementation, we do these silent trials, where we test out the solution in, say, a hospital, before it goes into practice. Because we dont want to just say, hey, this tool works fantastically, were gonna implement it now. Being able to pilot it is extremely important.
People are understandably quite anxious about the state of Canadian health care. What do you see as the potential for these AI programs, whenever they do get rolled out?
I truly believe that AI has transformative benefits for patients. There is a machine learning model that can create radiation therapy treatment plans for patients with prostate cancer. That can take a clinician more than a day to develop, and the model produces plans within hours that are deemed to be as good as or even better, nine times out of 10. If I were a patient, this is exactly what Id want: something that creates efficiencies and frees up resources so that I not only have a personalized treatment plan soonerbut I get to spend more time with my physician. Thats extremely beneficial to a patients quality of life and the quality of care they receive.This interview has been edited and condensed.Next: The Forces That Shape Health Care for Black Women
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