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AI x ML in Medicine

INTRODUCTORY COURSE
2023

Welcome to our third introductory course in medical AI. Originally delivered live in 2023. It introduces the basics of AI, coding in Python, and applications of AI in medical specialties.

Section A

The Basics of Artificial Intelligence and Machine Learning Tools

Section B

Introduction to Python and Libraries

Section C

Artificial Intelligence in Medical Specialties I

Section D

Artificial Intelligence in Medical Specialties II

A1. Introduction to Artificial Intelligence in 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

A2. Introduction to Programming and 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

A3. 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

B1. Medical 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

B2. Introduction to AI Models and Application

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

B3. Privacy in Medical Applications of Artificial Intelligence

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

C1. AI Applications in Public Health

Learning Objectives (Students should be able to):

  1. Understand the scope of public health

  2. Provide examples of how AI has been applied in public health

  3. Provide examples of how AI might transform the future of public health

C2. Machine Learning and Infectious Diseases

Learning Objectives (Students should be able to):

  1. Understand the scope of infectious disease

  2. Provide examples of how AI has been applied in infectious disease

  3. Provide examples of how AI might transform the future of infectious disease

C3. Application of Artificial Intelligence in 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

C4. Introduction to Artificial Intelligence in 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

D1. Artificial Intelligence in 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

D2. Artificial Intelligence in Primary Care & How to read a AI in Medicine Paper

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

D3. Case-based learning, Conclusion & 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

Secton A
Section B
Section C
Section D
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