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AI in Healthcare

photo of a health professional looking at data set with the words: Innovate, Elevate, Optimize

AI is going to be a pillar of modern healthcare. Join the movement to redefine patient care.

In today's rapidly evolving medical landscape, staying ahead of the curve is essential for healthcare professionals. Designed to cater to a broad spectrum of health professional learners, our professional development course focuses on fundamental Artificial Intelligence concepts and ethics, while providing deeper perspectives for those eager to attain a comprehensive understanding of how Artificial Intelligence works. AI-augmented healthcare is a reality. Be the leader that transforms your practice and patients' lives!

Are you ready to lead the charge? Register for AI in Healthcare Online Course now!

Don't watch from the sidelines. Engage with the future of healthcare through our AI in Healthcare course.

Designed to help you augment your existing and future practice with AI, our one of a kind accredited course equips you with the knowledge and tools to navigate this exciting convergence. 

Crafted in collaboration with esteemed professors from three academic institutions and the expertise of course developers at Queen’s University Health Sciences, this unique and timely course is about harnessing technology to elevate patient care, improve outcomes, and optimize practice efficiency.

"Early Adopters" rate $795 CAD for 1 year access

Regular Fee $995 CAD after Dec. 31, 2023

Register now for AI in Healthcare Course!

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Catch a course sneak peak with our free video featuring the AI in Healthcare educators.

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Kim Sears

The AI in Healthcare Course brings the theory and content in AI together with practical application and excellent high quality learning materials. The thought-provoking interactive sessions will assist learners apply the content in a meaningful and engaging manner. The learning modules were designed to meet learning needs and provide the opportunity to advance safe clinical practice which is greatly needed within our current health care environments.

Professor Kim Sears | RN, PhD
School of Nursing | Health Quality Programs
Deputy Director, Queen’s Collaboration for Health Care Quality
Inaugural Reznick Scholar, Health Professions Education, Faculty of Health Science
Adjunct Faculty, Joanna Briggs Institute, Faculty of Health Sciences, University of Adelaide

Course Details


Created by an interprofessional team of healthcare professionals and AI leaders from three academic institutions

Key Themes

What is AI?

The Applications of AI
to Health Care

The Ethics of AI

Online & On-Demand

Complete 7 interactive modules on a flexible schedule within 1 year and earn a certificate of completion
Total: 15 learning hours

Accredited Learning

Certified by the Royal College for 15 Self-Assessment learning hours and certified by the CFPC for 15 Mainpro+ Credits in the Self-Learning Category.

Register for AI in Healthcare Online Course

Module 01: Introduction to Artificial Intelligence in Healthcare
After completing this module, you will be able to:

  1. Explain the perceptions of AI in healthcare.
  2. Summarize the current state of affairs of artificial intelligence and machine learning as it relates to healthcare applications.
  3. Identify common applications of AI in healthcare.
  4. Identify the strengths and limitations of AI in healthcare.

Module 02: Basics of Machine Learning
After completing this module, you will be able to:

  1. Identify elements and concepts associated with machine learning (ML) in healthcare applications.
  2. Distinguish between common ML model types and illustrate their application in healthcare.
  3. Discuss emerging healthcare applications of natural language processing (NLP) and computer vision subfields of AI.

Module 03: Principles of Machine Learning
After completing this module, you will be able to:

  1. Discuss key principles in training machine learning (ML) systems.
  2. Critically appraise whether an ML system is likely to perform as advertised in your practice.
  3. Apply your knowledge of ML system principles for enhanced communication and collaboration with ML practitioners to advance AI in healthcare.

Module 04: Decision Trees and the Intelligence of Deep Networks
After completing this module, you will be able to:

  1. Identify the kinds of automated decisions that are well suited to a decision tree approach.
  2. Explain how decision trees select data items (attributes) to use in decision making.
  3. Explain how multiple decision trees can be combined for better performance.
  4. Compare the intelligence of deep networks to human intelligence from a mechanistic perspective.

Module 05: Convolutional Networks and Transformers
After completing this module, you will be able to:

  1. Explain the roles of gradient descent and backpropagation in deep network training.
  2. Identify common strategies to reduce overfitting in deep networks.
  3. Explain how convolutional networks process images and why they learn efficiently.
  4. Explain how transformers process language and how they learn from large amounts of unlabelled data.

Optional: Module 05 Supplementary: Deep Network Code Examples

Module 06: Ethics of AI in Healthcare
After completing this module, you will be able to:

  1. Identify key ethical issues associated with the use of AI in healthcare.
  2. Define key ethical concepts relevant for evaluating AI applications in healthcare.
  3. Apply general ethical concepts to specific uses of AI in healthcare.
  4. Describe the epistemic limitations of AI in healthcare.

Module 07: Applications of Ethics in AI
After completing this module, you will be able to:

  1. Identify a range of ethical issues in real-world case studies.
  2. Assess the extent to which existing ethical frameworks are adequate, given the rapid pace of AI development.
  3. Apply the ethical concepts and frameworks from previous modules to novel problems raised by AI in healthcare.

The content developers for this course are located within the Toronto-Waterloo Corridor, which was recently ranked in the Top 20, globally, for technology-based start-ups working in AI, cybersecurity, fintech, healthtech, and sustainability. The region supports one of the largest technology innovation clusters in North America, second only to Silicon Valley in California. 

Doug Dittmer MD, President, Canadian Strategic Clinical Innovation Network Inc. (CSCIN)

James Tung PhD, Associate Professor, Mechanical & Mechatronics Engineering, Faculty of Engineering University of Waterloo

Bryan Tripp PhD, Associate Professor, Systems Design Engineering, University of Waterloo

Joshua August (Gus) Skorburg PhD, Assistant Professor of Philosophy and Co-Academic Director of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), Faculty Affiliate at the One Health Institute, University of Guelph; Adjunct Professor in the Fuqua School of Business at Duke University

Steve F. Cross PhD, Director of Strategic Initiatives in Health & Life Sciences at Conestoga College, Senior Research Fellow at Wilfred Laurier University, Adjunct Professor at University of Victoria and Vancouver Island University

This online at your own pace program was accredited by Queen’s University for 15 Self-Assessment learning hours for the Royal College and 15 Mainpro+ Credits in the Self-Learning Category for the CFPC.

This course is structured specifically for the healthcare professional and offers a general introduction to how the evolution of AI tools is applied currently and is envisioned to impact the future healthcare environment.

Regular Fee is $995 CAD

Register now for the "Early Adopter" rate of $795 CAD per learner.

Early adopter rate ends December 31, 2023. 

Register online

This program is non-refundable

After registering, you will be able to access the AI in Healthcare Course for one year. You can complete the 7 modules (approximately 15 hours of learning) at any time during this period.

Be the innovator your patients need – master Artificial Intelligence in Healthcare today

Register for AI in Healthcare Online Course

AI in Healthcare Educators in the News

February, 2020 - Royal College of Physcians and Surgeons of Canada

Task Force Report on Artificial Intelligence and Emerging Digital Technologies

AI and digital technologies will become fundamental to the practice of medicine. To continue to meet patient needs, physicians will need a basic understanding of the available technologies, a stronger background in mathematics and statistics, and the ability to find and understand health information from electronic sources.