Artificial Intelligence in Healthcare

Designed to cater to a broad spectrum of health professional learners, our professional development course focuses on fundamental AI concepts and ethics, while providing deeper perspectives for those eager to attain a comprehensive understanding of how AI works. 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.

Module 01: Introduction to Artificial Intelligence in Healthcare

Explain the perceptions of AI in healthcare.

Summarize the current state of affairs of artificial intelligence and machine learning as it relates to healthcare applications.

Identify common applications of AI in healthcare.

Identify the strengths and limitations of AI in healthcare.

Module 02: Basics of Machine Learning

After completing this module, you will be able to:

Identify elements and concepts associated with machine learning (ML) in healthcare applications.

Distinguish between common ML model types and illustrate their application in healthcare.

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:

Discuss key principles in training machine learning (ML) systems.

Critically appraise whether an ML system is likely to perform as advertised in your practice.

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:

Identify the kinds of automated decisions that are well suited to a decision tree approach.

Explain how decision trees select data items (attributes) to use in decision making.

Explain how multiple decision trees can be combined for better performance.

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:

Explain the roles of gradient descent and backpropagation in deep network training.

Identify common strategies to reduce overfitting in deep networks.

Explain how convolutional networks process images and why they learn efficiently.

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:

Identify key ethical issues associated with the use of AI in healthcare.

Define key ethical concepts relevant for evaluating AI applications in healthcare.

Apply general ethical concepts to specific uses of AI in healthcare.

Describe the epistemic limitations of AI in healthcare.

Module 07: Applications of Ethics in AI

After completing this module, you will be able to:

Identify a range of ethical issues in real-world case studies.

Assess the extent to which existing ethical frameworks are adequate, given the rapid pace of AI development.

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 offers a general introduction to how the evolution of AI tools is applied currently and its ethical considerations with examples related to health sciences.

We are currently refining the focus of this course to better align with the needs of our learners.

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Artificial Intelligence in Healthcare

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