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.
