AI in Healthcare: Mastering the Essentials

Prepare Yourself for AI – Get the Essentials

Artificial Intelligence is no longer a futuristic concept; it’s reshaping industries today. From automating complex tasks to providing predictive insights, AI is redefining modern practices across sectors. AI can enhance diagnostic accuracy, improve patient management, and streamline administrative tasks – but you must first understand how it works before you use AI in your practice.

Our professional development course offers a deep dive into AI concepts, machine learning principles, and ethical considerations, equipping you with the knowledge to prepare you to leverage AI. Learn how to prepare to navigate AI's vast potential, understand its applications, and make informed decisions that will set you apart in your field.

Learn How to Prepare to Navigate AI’s Vast Potential

Before you can begin to use AI, you must first understand its applications, benefits, and disadvantages. This accredited course begins by breaking down the language of AI, explaining its workings in an understandable way. Through working examples and models, you'll learn how AI reaches its conclusions and its limitations. We also delve into the ethical considerations, helping you balance trust and scrutiny before you consider using AI in your practice.

Crafted by AI Experts and Interprofessional Health Teams

Developed in collaboration with esteemed professors and AI leaders from top academic institutions, including Queen's University Health Sciences, our course combines cutting-edge research with Healthcare specific examples. Course creators have aligned the learning material with key findings and recommendations of the RCPSC Task Force Report on AI and Emerging Digital Technologies.

Special Offer:

Limited Time Early Adopters Rate
For a limited time, enroll in our course at a discounted rate of $745 CAD.
Enjoy the flexibility of a fully on-demand course and customize your learning path.
Regular fee: $995 CAD

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Module Details

Customizable Learning Path

Our course is fully on-demand, granting you the freedom to learn what you want, when you need it. Customize your learning path with modules that align with your interests and schedule, earning accreditation certificates as you progress.

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

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.

Content Experts

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

Accreditation Details

This online program is accredited by Queen’s University for 15 Section 3 - Self-Assessment learning hours for the Royal College, 15 Mainpro+ Credits in the Self-Learning Category for the CFPC, and provides a certificate for 15 hours of professional development for all health professions.

Intended Audience

This course offers a general introduction to how the evolution of AI tools is applied currently and its ethical considerations. This course will be of interest to health professionals or administrators looking to learn the essentials before considering implementing AI in practice, or those interested in developing AI systems for healthcare.

Fees

Early Adopters Rate: $745 CAD (Limited Time)
Regular Fee: $995 CAD

Register Today

This program is non-refundable

This course brings the theory and content in AI together with health-based examples and excellent high quality learning material that walks you through the science of AI. The thought-provoking interactive sessions will assist learners in mastering the essentials. The modules were designed to meet learning needs and provide the opportunity to prepare 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

February, 2020 - Royal College of Physicians 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.

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AI in Healthcare: Mastering the Essentials