Training > Pillar three

AI

Artificial intelligence has already transformed many industries and is about to do the same to healthcare. However, there is a gap between real world clinical care and AI which this training aims to bridge. These three modules will appeal to those interested in evidence-based medicine, extracting knowledge from large-scale data and applying AI to improve patient care.
This training is led by the AI Centre for Value Based Health care (AI4VBH) which is a cross-institutional initiative pooling expertise from across the healthcare ecosystem, including technology companies to deliver AI, data science and advanced research into clinical practice. The AI pillar is partnered with NVIDIA on Cambridge-1, the UK’s most powerful supercomputer dedicated to healthcare and life sciences research, which will focus on solving large-scale healthcare and data-science problems.
AI

Summary of Modules

Arrows indicate the suggested flow of learning.

Demystifying AI

Now Live 

C

Python – Software Carpentry

Coming Soon

C

Applied AI

Now Live

Demystifying AI

This introductory 10 hour course will cover concepts such as:

  • What AI is and how does it works.
  • How can AI be used to support healthcare.
  • The ethical and legal challenges of AI technologies.

Participants will then be able to look in more detail at the following:

  • General clinical applications, for those in clinical roles looking to use AI to improve patient care
  • Radiological applications – focused on imaging and radiological report data.
  • Non-clinical specialisation for those working in non-clinical roles in a health care setting such as engineers, non-clinical scientists, technical hospital professionals and managers.

By completing this module you will meet 21 of the capability statements in the Artificial Intelligence (AI) and Digital Healthcare Technologies Capability Framework. This framework outlines the skills and capabilities that will allow health and care professionals to work effectively in a digitally enhanced environment.

The content in this module covers skills and capabilities in the following domains:

  • 3.1 Ethics (e,f,g)
  • 4.1.1 Management (d)
  • 4.1.2 Leadership (e)
  • 5.1.1 Data Collection (e)
  • 6.0 Artificial intelligence (a,b,c,s,e,f,h,I,j)
  • 6.1 Machine learning and Natural Language Processing (a,d,e,f)
  • 6.2 Using and Implementing AI systems (d) 
  • 6.3 Evaluating AI systems (f)
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Software Carpentry with Python

Leading on from Demystifying AI or for participants with a base level understanding of AI already this course will give participants an introduction to writing their own code to be used on AI platforms. Over four weeks participants will be taught Python fundamentals including:

  • Data visualisation
  • Loops
  • Functions
  • Errors and exceptions
  • Debugging
  • Data I/O

Due to be live by May 2022

 

Closed: we are not currently accepting applications for this module.

Applied Artificial Intelligence

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Are you looking to apply your understanding of AI to real world health problems? Applied AI could be the module for you.

The use of AI in healthcare is expanding and its use with image data such as X-ray, MRI and other medical images is not exempt. This intermediate level module aims to equip you with both the foundational knowledge and practical coding skills to ensure that you feel confident applying deep learning to real world health care problems and adapting to this fast paced field as it moves forward in future.

This module is for you if:

  • Are interested in applying deep learning and AI to healthcare images.
  • Wanting to expand your current knowledge of AI
  • Already have a working knowledge of Python including classes and can use packages such as numpy.
  • Have an understanding of mathematical concepts such as first order partial derivates and matrix algebra.

Learning aims:
On completion of this module 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 module 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.

Technology required:
We will be 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 module.

By completing this module you will meet 6 of the capability statements in the Artificial Intelligence (AI) and Digital Healthcare Technologies Capability Framework. This framework outlines the skills and capabilities that will allow health and care professionals to work effectively in a digitally enhanced environment.

The content in this module covers skills and capabilities in the following domains:

  • 6.0 Artificial intelligence (h,K)
  • 6.1 Machine learning and Natural Language Processing (e,L)
  • 6.3 Evaluating AI systems (i)