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AI in Action: The AI landscape at Sunnybrook

Dr. Anne Martel
Written by Monica Matys

While the idea of artificial intelligence (AI) may seem new to many of us, researchers like Anne Martel have been working with it for decades. As a Senior Scientist, Sunnybrook Research Institute (SRI) and Tory Family Chair in Oncology at Sunnybrook, Martel gives an insider’s perspective to this evolving field as part of our ongoing AI In Action series.

AI is a huge area with many applications. What’s happening at Sunnybrook?

It’s such a vast field, and there is so much going on at Sunnybrook. A lot of the AI research we’ve done at SRI has focused on getting information from pathology and radiology images, like x-rays and scans. We have developed methods to pinpoint disease in an image, and that helps pathologists and radiologists do their jobs better and faster. This also identifies the best treatment for each individual patient. When data from thousands of patients are fed into AI algorithms, that can identify patterns to help treat other people with similar diseases. Historically, finding these patterns would have taken years to do. AI lets us do this more quickly and accurately.

We are also taking advantage of the amazing progress made in developing AI models that are capable of understanding text. When a patient comes into hospital, we are collecting data at many points; the initial conversations at check in, during blood work, imaging and pathology, as well as when treatment is prescribed. All of this data helps us understand each particular patient’s condition, but right now research fellows have to comb over written notes to extract the relevant information. We can harness the recent advances made in AI to carry out this laborious work automatically.

How new is AI at Sunnybrook?

The techniques behind the imaging data collection I mentioned, and the principles of machine-based learning, were developed decades ago. My research team built an AI algorithm capable of analysing breast magnetic resonance images over 15 years ago, and we have continued to explore and develop new techniques ever since.

A few years ago, we gained international recognition after developing the first digital pathology foundation models to exist, meaning we took over one million specimen images from public databases and trained an AI model to understand patterns in what it was seeing. That now helps us figure out what disease a patient may have, or if they are responding to treatment, based on wider patterns. It’s also made it possible for us to develop more accurate AI models more quickly.

AI awareness among the general public and clinicians has increased greatly, and this has led to an eagerness to explore how it can improve patient care.

Some people may be fearful of AI. What do you say to them?

When people think of AI, they often think of ChatGPT and those kinds of applications. At Sunnybrook, we won a Canadian Foundation for Innovation (CFI) grant a few years ago which helped us build a state-of-the-art AI computing platform with enough power to allow us to train and run AI models within Sunnybrook’s secure environment. We are very aware of the importance of protecting each patient’s personal health information, and the platform we use ensures that.

What will AI deliver to patients in the next 10 years?

While much of Sunnybrook’s work in AI still isn’t at the point of directly affecting patients yet, that will hopefully change soon. One shift could be patients seeing faster action. For example, we’ve developed an AI algorithm that can find tiny regions of tumour cells in microscopy images. This could reduce the time patients have to wait for results after surgery. One of my colleagues has developed a model to identify brain bleeds in CT scans, so patients at risk can be seen immediately without having to wait for the scan to be read.

Another area would be personalizing medicine, where using AI could help direct what type of treatment or surgery would be optimal for each patient based on their unique circumstances.

AI may also help us use hospital resources more efficiently, and is a real priority at Sunnybrook. Things like using AI to figure out what bloodwork each patient needs based on their condition, which can cut down on unnecessary tests.

Are there downsides to AI?

It’s important to remember that AI is not magic; the algorithms are only as good as the information we feed into them. If an AI model is only trained to look at one group of patients, for example, there will be a built-in bias if applied to others. We need to train our AI models around a varied patient population that reflect the realities of our communities.

AI data also needs to be continually updated. Imagine that we had an AI model that was trained in 2018 to predict whether or not a patient has pneumonia. Today, in a post-COVID world, that model wouldn’t work anymore.

What about the human touch?

AI won’t replace humans in health care. It’s just a tool. AI can be a powerful aid, but we’ll still need the expertise of our clinical staff to understand how we best use it.

About the author

Monica Matys

Monica Matys is a Communications Advisor at Sunnybrook.

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