Canada promised to watch its doctors train. AI could make that happen

Samir Grover is a gastroenterologist and the executive vice-president of academics at Scarborough Health Network, and the Pialis Family Chair in Education at SHN Research Institute. In 2017, the Royal College of Physicians and Surgeons of Canada launched Competence by Design, the most consequential reform of Canadian physician training in decades. The premise was simple: We can define all the elements that a graduating specialist should know or do in practice, and we should only graduate them when a supervisor has actually watched them do the work. Readers who are fans of the HBO show The Pitt, will understand the thinking behind this. Resident training is vital to producing high-quality doctors, and having a competent supervisor such as Dr. Robby observe them is the best way to ensure they're performing procedures correctly and safely. Opinion: A message to Canada's new medical students But after nearly a decade, most doctors in Canada are still not being observed in training. This could impact patient care. I trained as a specialist in this country. In the entirety of my residency, consisting of thousands of patient encounters, fewer than 10 were formally observed by a supervising physician. I completed my training despite rarely being directly assessed on my performance. That remains true for many residents today. This is not because supervising doctors are failing. The attending physician supervising a resident may also be running between consults, fielding pages from the operating room and covering overnight calls. We ask supervisors to produce real-time observational assessments, while rushed physicians often only have time to review, not observe, their trainees. We designed a system that depends on observation, without making observation feasible. At Scarborough Health Network, we have made significant strides to change this, and have been aided greatly by AI. Much like the fictional Pittsburgh Trauma Medical Center in The Pitt, we are a community teaching hospital caring for 850,000 people in the Greater Toronto Area, with hundreds of learners rotating through our care settings each year. In building and testing AI tools for observation, feedback and simulation, our aim is to create a made-in-Scarborough solution that all teaching hospitals in Canada should consider implementing. Why Canada's largest pediatric hospital is betting on artificial intelligence In Canadian primary care today, AI 'scribes' are commonly used to transcribe visits and produce draft notes during encounters between physicians and patients. With patient consent and clear governance, every student- or resident-patient interaction can be recorded and transcribed. Alongside a supervising physician, an AI system reviews these encounters against competency frameworks and relays specific feedback directly to the medical learner. Instead of fewer than 10 observed encounters across a career, every encounter can become a teachable one. At our Scarborough hospitals, AI will help determine where our incoming physicians still need further training. For example, it can audit a resident's case logs and reveal trends of deficiencies that may not be apparent initially. Two systems we are currently using include virtual standardized patients simulations, which use video avatars and voice models to replicate clinical scenarios for training physicians. We are also testing a performance assessment system that uses AI for medical training, with unobtrusive audio and video to evaluate performance. The goal is creating a more objective view of how a trainee performs over time, rather than relying on fragmented, memory-based evaluations. Together, these approaches move training from occasional observation to continuous, structured assessment: capturing what happened, identifying what is missing, and allowing trainees to practice until they improve. None of this is risk-free, and the guardrails matter. The AI programs we use have patient consent and full compliance with provincial privacy laws in Ontario. Faculty must remain firmly human-in-the-loop, with AI never replacing professional judgment and teaching. Models must be audited for bias, particularly in how they assess trainees from underrepresented backgrounds. AI's role is only to help supervising physicians teach, not replace them. Imagine that a resident sees a patient at 11 p.m., who did not understand the instructions for discharge from the emergency room. The next morning, they sit with the supervisor, and review the conversation. The AI points to moments where the understanding did not occur. The supervisor and resident rehearse it. The following week, using a virtual patient simulator alongside real-life demonstrators, the resident practices discharge conversations repeatedly until those techniques become routine. In this way, better training allows for better care. It was what Canadians were promised years ago. It's time that the system delivered.