\u201cAI models are becoming more intelligent every day.\u201d<\/p>\n<\/blockquote>\n\n\n\n
If a patient presents with chest pain, a doctor would ask all sorts of questions, including medical history and symptoms, but Mago says the AI assistant could say: \u201c\u2018Hey, doctor or nurse practitioner, you missed asking this question,\u2019 or recommend care approaches or suggest an ambulance be called.\u201d The ultimate goal is to improve diagnostic accuracy, patient outcomes and safety.<\/p>\n\n\n\n
\u201cFor rural emergency departments, where there is limited access to critical care, these AI-based approaches can help alleviate a lot of pressure,\u201d says Mago, a member of Connected Minds and the Centre for AI & Society at York.<\/p>\n\n\n\n
The model, once complete, still needs to undergo testing in a clinical setting, but down the line these types of AI models could help manage and diagnose any number of ailments, including strokes or diabetes.<\/p>\n\n\n\n
\u201cThere are some very exciting things happening right now in the field and a rush to leverage the potential of these systems to improve health care, which would eventually include treatment options and predicting disease progression and outcome,\u201d says Mago, whose health related research has garnered some $3.5 million in funding, including from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council and the Public Health Agency of Canada (PHAC).<\/p>\n\n\n\n
With such a surge in interest, Mago acknowledges, comes the necessity to ensure AI is unbiased, ethical and adding benefit, rather than harm, to patients.<\/p>\n\n\n\n
\u201cAI models are becoming more intelligent every day. The goal is to figure out how to infuse them with emotional intelligence and cognition, and to make sure they are safe,\u201d says York Research Chair in Safe AI for Health Equity, Elham Dolatabadi, who recently received funding through the Canada Foundation for Innovation\u2019s John R. Evans Leaders Fund to start the Health Equity and AI Lab (HEAL).<\/p>\n\n\n\n Over the next five years, she will be part of a team building a human-AI complementary system that combines human brain power with cognitively robust and emotionally intelligent AI for use in health care and mental health.<\/p>\n\n\n\n She agrees agentic AI holds the promise of being a game changer for improved health care, which is why much of her current focus is on creating toolkits and pipelines to evaluate these systems before being deployed. These multi-agent models are more complicated to assess than non-agentic generative AI models where it is easier to see if the outputs align with expectations.<\/p>\n\n\n\n When dealing with several agents in a model, if one is biased, perhaps toward a certain demographic, it could throw off the accuracy and safety of the output, the diagnosis or prognosis. <\/p>\n\n\n\n \u201cHackers are another issue. They can attack one of the agents in the group or infuse a faulty agent into the system, which may corrupt how the system thinks, or push it into hallucinating. That\u2019s something many will find surprising, but AI not only lies; it hallucinates,\u201d she says. In both cases, the model outputs something that looks factual, but is not.<\/p>\n\n\n\n \u201cHallucination is very complicated to understand, but we are working on a dynamic pipeline for hallucination evaluation, as well as pipelines for AI agentic models in mental health, acute care and outpatient care. These are across different dimensions, clinical values, behavioural values or cognition, and emotional intelligence, so they align with human values,\u201d says Dolatabadi of York\u2019s Faculty of Health and a faculty affiliate of the Vector Institute.<\/p>\n\n\n\n There can be different agentic systems for each health-care application. What will work for mental health care will not work for acute care. \u201cWe also need to ensure the output is not something AI made up because it didn\u2019t know the answer.\u201d<\/p>\n\n\n\n Gender, ethnicity, race and skin colour can all affect accuracy. Anyone who falls outside of the average parameters is not always served well.<\/p>\n\n\n\n \u201cThat\u2019s the risk,\u201d says York Associate Professor Ian Stedman<\/a> of the School of Public Policy & Administration, Faculty of Liberal Arts & Professional Studies. He is cross-appointed to York\u2019s Osgoode Hall Law School where he graduated with a PhD. \u201cDifferent subpopulations are not always captured as well with AI models. The question becomes, as a doctor do you still deploy the model if you know that it\u2019s going to catch 80 per cent of European diseases and 10 per cent of sub-Saharan African diseases?\u201d It\u2019s a question Dolatabadi ponders often as she develops evaluation tools, suggesting the doctor needs to be aware of any limitations so they can make adjustments.<\/p>\n\n\n\n Despite the current shortcomings, Stedman too holds out great hope in the power of AI for the future of health care, specifically in how AI can help better leverage genome sequencing. As someone who lives with a rare genetic condition, he is a prime example of a person whose health data might not yet be captured by larger AI models, as is his daughter who inherited the same gene mutation.<\/p>\n\n\n\n It took 32 years of searching before he finally had a diagnosis and learned the name of his rare disease, which allowed him to access appropriate medicine. Stedman says that getting a diagnosis felt, at the time, like winning a lottery.<\/p>\n\n\n\n There are more than 8,000 known rare diseases that affect one in 12 Canadians, but only about five per cent of them have an effective therapy, with even fewer having access to available therapy. Stedman really is among the lucky.<\/p>\n\n\n\n In his vision of the future, he says, AI plays an outsized role in health-care systems. With its ability to speed up genomic data interpretation, unpack clues to rare disease diagnoses and generally help in the understanding of each individual\u2019s needs better, he believes that getting AI right will be non-negotiable in moving toward the ideal of having personalized health-care systems.<\/p>\n\n\n\n \u201cIf you look at the power of genomics in the context of its ability to improve care, it comes from unlocking the data through genomics sequencing. If we can do that with big data analytics and in an everyday clinical setting, we can change health care,\u201d he says. \u201cThe challenge is all the legal stuff, which is where my unique hat comes in.\u201d<\/p>\n\n\n\n Stedman brings a particular set of skills, knowledge and experience as a patient to the realm of AI and health. He is on the executive of the Centre for AI & Society<\/a> and Connected Minds<\/a> at York and the Chair of the advisory board for the Canadian Institutes of Health Research\u2019s (CIHR) Institute of Genetics.<\/p>\n\n\n\n \u201cThe potential of AI is bigger than people imagine.\u201d<\/p>\n<\/blockquote>\n\n\n\n![\"\"](\"https:\/\/www.yorku.ca\/ascend\/wp-content\/uploads\/sites\/689\/2025\/12\/Diagnosis-Chat-Elham.jpg\")
![\"\"](\"https:\/\/www.yorku.ca\/ascend\/wp-content\/uploads\/sites\/689\/2025\/12\/Diagnosis-Chat-Ian.jpg\")
\n