Applied Artificial Intelligence for Health Research

Course Overview

  • Written by experts
  • Intermediate
  • 100% online
  • Video content
  • Multiple choice quiz
  • Completion certificate
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About this course

Should I Study this Course?
The goal of this course is to equip you with sufficient foundational knowledge, but primarily practical coding and engineering skills to ensure that you feel confident applying deep learning to real world health care problems and adapting to the fast paced field as it moves forward in future.

This is an intermediate level course, requiring you to have a working knowledge of Python, including classes and the ability to work with packages such as numpy. It will help you to have studied mathematics at high school, since this course uses concepts such as first order partial derivatives and matrix algebra in its teaching.

This course teaches how to work with image data, which has a richer structure than many other types of data. Thus it is important for you to take the time to understand theses structures, and how they are transformed prior to being used in deep learning models.

Learning Aims:
On completion of this course you will :

  • Be able to implement simple fully connected networks from scratch in Python. and common deep networks in PyTorch
  • Be exposed to a wide range of deep learning applications for healthcare and be comfortable applying the ideas raised in the course to your own research
  • Understand the strengths and limitations of deep learning and how to adapt modern networks to work on challenging real-world medical imaging data
  • Understand how to validate models effectively and troubleshoot problems with their architectures 

How this Course is Delivered
The course topics are delivered over a series of interactive lecture videos and practical assignments using Jupyter Notebooks created for you to run on Google Colabatory (Colab). Full details on accessing Colab are given in the Get Started section of this course
At various points in this course you are required to attempt a short interactive Quiz. You must answer all the questions correctly before you will be able to move on to the next part of the course; however you can have as many attempts at each quiz as you need. When you pass the final quiz, you will be able to download a completion certificate (CPD accreditation pending).

Course authors and designers

Jorge Cardoso

Academic Lead
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Emily Robinson

Academic Co-lead
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Mariana Ferreira Teixeira Da Silva

Online Course Developer





Pedro Borges

Content Developer
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John Langham

Online Course Developer

Andre Crawford

Learning Technologist | FdSc, BSc, PGCE, MA
Andre has over 15 years of experience in education, specialising in UX/UI design and content development. As a qualified teacher, he has a background in developing comprehensive college-wide training and development strategies based on skills-gap analysis and creating analytics dashboards for program leaders. Andre is particularly adept at crafting online and blended learning programs, ensuring effective and engaging educational experiences.
This module is supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and
Maudsley NHS Foundation Trust, King’s College London, and the King's Computational Research, Engineering and Technology Environment
(CREATE).
The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health