AI in Practice for Medical Residents

Artificial Intelligence is no longer a futuristic concept—it’s shaping the practice of medicine today. As medical residents, you are at the frontline of this transformation. The University of Toronto's Temerty Centre for AI Research and Education in Medicine (T-CAIREM) can help.

T-CAIREM's upcoming AI in Practice for Medical Residents weekend mini-course offers practical, hands-on exposure to the tools, opportunities, and implications of AI in your medical training and future practice. You’ll leave with strategies to improve productivity during residency and insights into the role AI will play in shaping your specialty.

No coding experience is required. This is not a course designed to develop an AI tool. Instead, it focuses on current practical applications of AI in healthcare and their clinical implications. NOTE: A certificate of completion will be provided for participants who pass the end-of-course quiz.

TIMELINE: Three hours per day on Saturday, February 28, 2026, and Sunday, March 1, 2026, from 1:00pm to 4:00pm ET.

COST: CAD $250 + tax (until February 15, 2026, at 11:30pm ET).

After February 16, 2026, the fee increases to CAD $425 + tax.

REGISTRATION DEADLINE: February 22, 2026 (Sun.) at 11:30pm ET

About the Instructors

Dr. Nihal Haque
T-CAIREM Education Faculty Affiliate, Temerty Faculty of Medicine
Dr. Haque is a physician specializing in geriatric medicine at North York General Hospital in Toronto, Canada, and an Adjunct Assistant Professor at the University of Toronto. He has obtained certification in Artificial Intelligence (AI) in Healthcare from the Michener Institute and Harvard T.H. Chan School of Public Health. He is a physician representative of his hospital's AI working group and is part of an interdisciplinary team that recently obtained federal funding through Canada Health Infoway to develop AI solutions to reduce healthcare worker burnout. With a focus on improving patient outcomes through healthcare innovation, Dr. Haque also leads AI-driven research in other areas, including dementia care and medical education in Geriatric Medicine. He is also actively involved with the TCAIREM education committee as a faculty advisor on AI in medical education.

Gemma Postill, BMScH
T-CAIREM Education Trainee Co-Lead & MD/PhD student, Temerty Faculty of Medicine
Gemma Postill leads the educational programming at T-CAIREM alongside her student co-lead Abhishek. Gemma is in the MD/PhD program at the University of Toronto. Through her research, Gemma aims to develop and implement artificial intelligence solutions to improve patient health outcomes. When not working, Gemma can be found cycling, baking, reading, or travelling!

Course Overview

Day 1: Clinical language-based tools

– Introduction to AI types

– How do NLP and LLM work?

– AI search engines

- Use-cases

- Custom chatbots

- Clinical activity

Day 2: Using multimodal AI tools in healthcare

- Introduction / clinical activity

– Productivity tools

- Non-generative tools with clinical applications

– What makes an AI model of “good quality”?

– Liability, privacy, and medical issues with using AI

Learning Objectives

By the end of the course, the learner will be able to understand and evaluate an AI technology for incorporation into their day-to-day practice through the following learning objectives:

• To describe the fundamental concepts of artificial intelligence, including the distinction between AI and traditional computer programs, and explain the practical applications of generative AI and Large Language Models (LLMs) in clinical practice.

• To identify key AI algorithms relevant to medical practice and how they are used to solve day-to-day clinical problems.

• To analyze the medicolegal and ethical issues associated with AI use in clinical practice, incorporating current CMPA, CPSO, and data privacy guidelines.

• To develop a structured approach to assessing AI tools for clinical use in your clinic or hospital setting, including considerations for data privacy, patient consent, and workflow integration, while identifying readily available AI applications that can enhance day-to-day medical practice.