Reimagining Healthcare: Unleashing the Power of Artificial ...

"These Are Life-or-death Scenarios": Meet The Scientist Bringing AI To Toronto Hospitals

"These are life-or-death scenarios": Meet the scientist bringing AI to Toronto hospitals

Bo Wang, the new chief AI scientist at UHN—Canada's largest hospital network—on embracing tech, cutting down wait times and whether robots are coming for health care workers' jobs

Photo courtesy of University Health Network

In the near future, patients at Toronto's hospitals will be greeted by the stiff metallic smile of a scalpel-wielding robot. Okay, just kidding—but it's safe to say that the field of medicine will soon become a little more Jetsonesque. Case in point: the University Health Network (UHN), Canada's largest network of research hospitals, just appointed its very first chief AI scientist, Bo Wang, who has been tasked with overseeing the implementation of AI tools in hospitals across the city.

Both Wang and UHN have some experience in this field. Earlier this year, UHN launched its AI Hub, co-led by Wang, which aims to bring scientists and clinicians together to develop new AI technology. Wang's latest gig makes him the very first chief AI scientist in the country. Here, he breaks down what the job entails, why hospitals have been slow to adopt machine learning and why he doesn't expect physicians to be as resistant to the tech as, say, Hollywood screenwriters.

You have a PhD from Stanford. Is it safe to assume that you're a PhD-style doctor rather than an MD?Ha—correct, I'm not a medical doctor. My background is in computer science and computational biology.

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Is it unusual for someone who's not a medical doctor to be the chief of something at a hospital?Well, in this case the role is very new. But I do work with a lot of medical doctors, of course. I meet with them to discuss their burning questions about AI and assess how machine learning could help address challenges they're facing. Those sessions go both ways—they tell me what they care most about, and we tell them what's possible. Doctors are fantastic people. It's amazing that they manage to do any research given their busy schedules.

You must have fairly packed days as well, with the shiny new role. What does the chief AI scientist at UHN do?I have three main focuses. The first is research—developing AI models that can parse health-related data, publishing papers and working with other hospitals. The second is education. There's a lot of hype around AI. I want to demystify tools that can assist with note-taking and administrative tasks, and I want to help physicians know what's true and false about more long-term possibilities, such as AI that can assist with surgical procedures. The third pillar is what we call adoption. There are lots of research papers on AI out there. While there are a few AI tools being used in UHN hospitals, we're not seeing widespread adoption in Canada. As we know, our health care system is in crisis. If AI can help improve the system, it's important that we explore that.

Some hospitals in the US, like Mayo Clinic, are already using AI to help diagnose heart disease and strokes. Why do you think Canadian hospitals have been so hesitant?Well, the US government has invested a lot into basic research on AI in hospitals. Their investment is the highest in the world, which is why they're the leader in this field. In Canada, we've fallen behind in terms of our investments in new science. In addition to that, AI can digest huge amounts of data, but it's bulky and complicated. There can be a lack of trust from physicians because they don't fully understand how it works. Then there are ongoing legal issues, like ensuring that our infrastructure for storing patient data continues to abide by Canada's privacy laws, and of course ethical issues around the possibility of biases in the data we use to train AI.

Right, we know that AI tends to take on human biases. For example, an algorithm used to predict crime in the US disproportionally labelled Black defendants as "high risk."We've seen that with algorithms used for matching donor organs with potential patients. They tend to match more men than women because the data that was used to train them was mostly collected from men. These are life-or-death scenarios, so it's extremely important that we develop unbiased tools.

How do you troubleshoot for that sort of thing?First, you try to collect data in an unbiased way—making sure you have sufficient representation of different kinds of patients. Toronto hospitals have an advantage with that because it's such a diverse city. Then, we need to be testing these tools in more than one setting. At UHN, we try something out in one of our hospitals and then bring it into other hospitals or even reach out to colleagues in the US or Europe. That way, we can verify that it works well for different groups of people.

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How do you imagine AI will change regular people's experiences in the health care system? I'm guessing fully robot-operated surgeries are still a ways out.Yes, but there are some real examples of how the patient experience is already changing. One is this app we co-developed called Medley. It's a regular mobile app—you can get it at the App Store—and it helps nurses at the Peter Munk Cardiac Centre remotely monitor patients with heart conditions. A patient inputs certain measurements at home, and the app uses AI to flag any abnormalities or changes. Then a nurse can call the patient to check in. Another example is a recent product we developed at UHN, which is a language model, sort of like ChatGPT. We connect it to a microphone, and it can listen to a conversation between a doctor and a patient. When they're done talking, the model can immediately summarize that conversation into clinical notes. That way, instead of spending the whole appointment glued to their computer, typing, doctors can actually look their patients in the eyes.

Could these kinds of changes mean that patients won't be stuck waiting in emergency rooms for 12 hours?That's the goal, yes.

In both of your earlier examples, physicians are still in the equation. Is it safe to say you're not working on an algorithm that could replace them entirely?Exactly. We're not making decisions for doctors; we're helping them make more-informed decisions while spending more time with their patients. AI is the perfect tool for precision medicine. For example, there is this concept called the "digital twin." We have all this data, which represents millions of past patients. So, when a new patient comes in, we can find their digital twin from the database—the person who is most similar to them based on, say, genomic profiling and medical history. We can learn from their twin's experience. If they tried certain drugs that failed, maybe we should avoid those drugs. If they benefited from a specific combination of treatments, maybe we should try similar treatments for this new patient.

Okay, fast forward ten years. How do you hope to see hospitals using AI?One thing I'm excited about is the potential for medically equipped AI training. We have a shortage of medical staff due to a lack of decent training programs. AI could be a part of the solution there. Language models could be used to simulate doctor-patient dialogue, which trainees could critique. Virtual reality could create 3-D models of tumours, which surgical residents could interact with. Also, we can develop AI to synthesize vast amounts of literature and help doctors make the most informed decisions. But I don't think current AI models are accurate enough for that yet.

In some industries, like the movie-making biz, there has been a lot of pushback against letting AI take on formerly human responsibilities. Should health care workers be worried for their jobs?I know that's a fear we hear a lot about, but I haven't seen it in health care. The president of the American Medical Association said, "It is clear to me that AI will never replace physicians—but physicians who use AI will replace those who don't." I think that's very accurate, because AI can greatly enhance a doctor's ability to manage their patient care and daily workflow.

You don't think there will be any Luddite hold-outs?There's a famous 2021 survey from the Peter Munk Cardiac Centre, which found that over half of physicians are suffering from burnout. Cumbersome tasks like note-taking are some of the driving factors behind that, and those are things we know AI could handle. Anything we can do to lower that percentage would only benefit our health care system.

This interview has been edited for length and clarity.

Can AI Improve The Mental Health Of Overworked Employees?

'This will be the most transformative technological development in our lifetime,' says CPO

The potential uses for AI are still largely unknown but the technology is advancing so much that its impact on the world of work will be immense, according to a senior HR leader.

"This is an existential component, and it's part of the strategy for companies going forward: this will be the most transformative technological development in our lifetimes," says Mary Alice Vuicic, chief people officer at Thomson Reuters in Toronto.

This belief was backed up by a Thomson Reuters survey done with more than 1,200 professionals from the legal, tax, accounting and risk fields between May and June, of workers in Canada, U.S., U.K. And Latin America.

The survey revealed that 67% of respondents feel AI will have a revolutionary impact on their professions over the next five years, while another 66% forecast it will create new career paths.

And this impact could be positive for these workers, says Vuicic.

"Overall, we found that professionals obviously see the potential for AI and believe that AI will transform their professions. The view is that this has the potential to improve their work experience."

Another survey done by LinkedIn found that a large percentage of workers are "overwhelmed" by the advent of AI.

Using tech tools to impact mental health

Deploying AI to do tasks that might be repetitive or boring can help, which can help employees' wellbeing, says Vuicic.

"So reducing long working hours and the fear of making errors, which is so important in the work that professionals do for their clients, both of those things have a negative impact on the mental health of 80% of professionals, based on what was reported, and so AI is perfectly suited to address those."

For HR professionals dealing with the negative impacts of exiting employees, AI may prove a boon, she said.

"This will help to address the very real human-capital constraints that we have right now: there are not enough individuals entering the profession relative to the work and the number of people retiring and exiting. AI will enable non-credentialed people to do more of the work, and so that will ease some of the capacity constraints that have been pushing the longer hours and creating issues."

Using AI tools in HR

And while there is "mixed sentiment" about the power of AI, clear guidelines have emerged to help organizations take advantage of the technology.

"Number one is the responsibility of employers and partners to help develop, create the training and the environments where people can experiment and develop new skills," she says.

As an example, Vuicic's employer is employing the tool in the HR department to great effect.

"Today at Thomson Reuters, we're using this for drafting job descriptions, drafting postings, helping to develop a standardized view of the skills of the individuals; helping prepare interview questions for the interviewers, and there's so much more that can be leveraged. The tooling is developing very quickly in this area and firms and corporations are going to have to leverage them to improve."

For employees in these fields, there is an "individual responsibility, that professionals — and this is really critical — need to be very intentional about their development going forward," says Vuicic.

"So what are their strengths, weaknesses, opportunities, they have to develop a career plan to capitalize on their development so that they can truly unlock the potential of the positive impacts of AI?"

AI brings out curiosity and fear

Almost two-thirds (64%) of survey respondents feel that the technology will make their own skills more valuable in the future, and 24% believe it will happen within 18 months.

"I think, in general, there's equal parts curiosity, and then fear of the unknown and uncertainty," says Vuicic.

"So it's essential for organizations to be very transparent about where they are on this journey to incorporate AI into their processes and into their tooling, and move very swiftly on how they support individuals in upskilling and reskilling and creating – and this point is absolutely essential – creating places for people to safely and securely experiment with AI."

But the interest in the technology certainly exists, she says, citing a global learning day at Thomson Reuters focused on AI and machine learning – with around 7,000 participants.

"And we had thousands participating afterwards. The vast majority of people indicated that this was useful in their roles, and that they saw value in it. It since has become the hottest topic, the number one topic in all of our sessions, whether it's town halls, whether it's fireside chats with leaders, whether it's questions about the future of the organization," says Vuicic.

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Toronto Hospital Network Appoints Chief AI Scientist In Bid To Improve Health Care

Canada's largest network of research hospitals has appointed a chief artificial intelligence scientist to harness promising technology that has the potential to speed up diagnoses, improve and personalize patient care and shorten recovery times.

Bo Wang, whose expertise at the Toronto-based University Health Network includes machine learning and computational biology, is stepping into the role after the launch of UHN's AI Hub earlier this year. The hospital network says the hub brings together doctors and researchers who work with AI in areas including cancer and cardiovascular disease.

Wang will lead the research into how AI can use vast amounts of anonymized patient data collected from the Toronto area's diverse population to improve care. He said some AI applications he hopes to explore include development of personalized treatment plans and automated generation of clinical notes.

"The goal is to promote adoption of AI in health care," Wang said in an interview. "We have lots of research but adoption is quite rare, and I want to change that."

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UHN is not alone in its exploration of AI uses in health care. Other hospitals across Canada have been using the technology in limited ways, such as for analyzing the results of medical scans, with oversight from radiologists.

The ultimate goal is to create individualized treatment plans by having AI analyze vast amounts of information and identify patterns based on everything from genetic data to patients' symptoms, lab results and medications.

Wang said UHN would work with private companies to integrate their AI solutions into clinical practice, following approval of those technologies by Health Canada.

As a founding member of a Mayo Clinic data network in the United States, UHN would also have access to data sets from other countries including Israel and Brazil, he said.

Wang, who is also a faculty member at Toronto's Vector Institute, which specializes in AI, was one of the main developers of a demo model called Clinical Camel, which was trained on data from thousands of anonymized UHN medical records. It can summarize long conversations between doctors and patients into clinical notes within seconds, he said.

Health-care providers must approve the notes and they can also add information about a patient's mood or emotional state, Wang added. Doctors can also ask the chatbot questions about symptoms of certain diseases and diagnoses to inform their patient care decisions.

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However, the so-called generative AI model, still under development with researchers from the University of Toronto and McGill University in Montreal, needs improvements to make it more reliable. And Health Canada would have to approve the software to ensure accuracy and safety so it does not make wrong predictions about a diagnosis, said Wang, adding the regulator would also have to be satisfied that patient privacy is protected.

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Various companies are also developing similar language models to record consultations between doctors and patients and increase efficiency.

"It's not happening anywhere yet but we see lots of demos, lots of announcements from big corporations like Microsoft," Wang said of the technology.

UHN says it hopes to expand on the narrow AI applications already being used at its hospitals. At Princess Margaret Cancer Centre, for example, radiation treatment times have been slashed by nearly half in some cases, based on a predictive model built from UHN data on patient recovery after treatment, response to certain drugs and survival times, Wang said.

"This AI model can automatically decide what the optimized dose is for each radiation (treatment) for this particular patient and what's the time span between different radiation therapies. So, that maximizes your chance of survival and maximizes your post-treatment recovery," he said.

"The wait time for the patient is smaller, the radiation exposure to the patient is smaller, without sacrificing the treatment's effectiveness. We are looking at improvements of almost 40 to 50 per cent in radiation."

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Brad Wouters, UHN's executive vice-president of science and research, said that while AI presents an enormous opportunity in health care, there are "obvious concerns" related to patient privacy and safeguarding data.

That's why UHN will not share even its anonymized data with the Mayo network or vice versa, he said.

"What's shared, actually, are the algorithms and tools that train on the data," he said. "The data never actually leaves or is mixed or under the auspices of any other organization."

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