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Artificial Intelligence
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Machine Learning

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Medicine

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in

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Course

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2023

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Section A

The basics of artificial intelligence and machine learning tools

A.1. Introduction to Artificial Intelligence and Medicine

Learning Objectives (Students should be able to):

  1. Define Artificial Intelligence and Machine Learning

  2. Describe the potential role of artificial intelligence in medicine 

  3. Explain at a basic level why data type/quality is important for use in machine learning tools

lecture slides

A.2. Workshop session introducing Programming & Python

Learning Objectives (Students should be able to):

  1. Successfully create and run a python program in Google Colab

  2. Understand and use the basic syntactic elements of Python 

  3. Develop awareness of good coding habits such as readability and descriptive commentary 

Lecture Slides

A.3. Reviewing Major Python Libraries Relevant to AI

Learning Objectives (Students should be able to):

  1. List the major Python libraries used in AI

  2. Give examples of what each library can be used for

  3. Know where to find documentation for using specific libraries

LECTURE SLIDES

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Section B

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Introduction to python and libraries

B.1. Data and Preprocessing

Learning Objectives (Students should be able to):

  1. Understand the importance of data quality used in machine learning 

  2. List the basic medical data types

  3. Describe common data processing techniques used in AI

Lecture Slides

B.2. Introduction to AI models and applications

Learning Objectives (Students should be able to):

  1. List the commonly used AI models and provide examples of their application

  2. Know where to find documentation on using specific AI models

  3. Develop an awareness of the advantages and drawbacks of each AI model

B.3. Ethics, Privacy and Fairness

Learning Objectives (Students should be able to):

  1. Appreciate the complexity of the ethicolegal considerations when using AI in medicine 

  2. Explain the role of privacy in data collection and usage

  3. Describe how data bias can affect outcomes and fairness in AI implementations

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Section C

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Artificial Intelligence in Medical Specialties I

C.1. Public Health and Infectious Diseases

Learning Objectives (Students should be able to):

  1. Understand the scope of public health and infectious diseases

  2. Provide examples of how AI has been applied in these disciplines

  3. Provide examples of how AI might transform the future of these disciplines

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C.2. Neurology and Neurosurgery

Learning Objectives (Students should be able to):

  1. Understand the scope of Neurology and Neurosurgery 

  2. Provide examples of how AI has been applied in these disciplines

  3. Provide examples of how AI might transform the future of these disciplines

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C.3. Radiology, Pathology, and Cardiology

Learning Objectives (Students should be able to):

  1. Understand the scope of Radiology, Pathology, and Cardiology 

  2. Provide examples of how AI has been applied in these disciplines

  3. Provide examples of how AI might transform the future of these disciplines

X-Ray
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Section D

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Artificial Intelligence in Medical Specialties II

D.1. Genetics, -omics, and Oncology

Learning Objectives (Students should be able to):

  1. Understand the scope of Medical Genetics, Omics and Oncology

  2. Provide examples of how AI has been applied in these disciplines

  3. Provide examples of how AI might transform the future of these disciplines

DNA

D.2. Other applications of AI in medicine and Evaluation of AI/ML papers

Learning Objectives (Students should be able to):

  1. Provide examples of how AI has been applied in these disciplines

  2. Provide examples of how AI might transform the future of these disciplines

  3. Have an approach to reading a medical AI paper

Doctor

D.3. Case-based Learning and Wrap-Up

Learning Objectives (Students should be able to):

  1. Describe how AI has been applied to the given study 

  2. Provide examples of how AI might transform the future of this discipline

  3. Develop the ability to evaluate a medical AI paper

Reading Group
Section A
Section B
Section C
Section D

Registration

Now closed

Feel free to watch lecture vodcasts for your own learning! Registration for course credits is now closed. If you wish to receive credit for this course, please register next year.
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