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Clinical radiology Artificial Intelligence (AI): A blended learning programme (February, in-person)

Members: £375 - £550Non-Members: £650
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Led by RCR’s AI Education Committee Members:

  • Dr Susan Shelmerdine, Consultant Paediatric Radiologist, Great Ormond Street Hospital
  • Dr Mitch Chen, National Institute for Health and Care Research (NIHR) Clinical Lecturer in Radiology, Imperial College London
  • Dr Krit Dwivedi, National Institute for Health and Care Research (NIHR) Clinical Lecturer in Radiology, University of Sheffield

We would also like to thank Professor Andrea Rockall (Clinical Chair in Radiology, Imperial College London, and the Chair of RCR’s AI Working Group) for overseeing the development of this course.

Course overview

RCR Learning is excited to launch this new blended learning programme combining opportunities for peer-to-peer interaction with online self-paced e-learning material for a seamless and complementary flow of learning.

This course is for radiologists and allied healthcare professionals in radiology at any level of training or experience, who want to gain a global overview of Artificial Intelligence (AI) in radiology and what it means for the speciality.

Participants will receive access to two interactive e-learning modules (approximately taking 2-3 hours each to complete), as well as a workshop featuring AI experts in the field of radiology. This will help enhance your learning experience and allow you to network with colleagues who share an interest in AI in Radiology.

Course details

Launch of e-learning resource: Wednesday 15 November 2023
Online workshop: Thursday 18 January 2024 from 9:30 to 15:30 on Zoom

E-Learning Modules


Module 1 - Introduction to AI in radiology and healthcare


  • Status and role of AI in radiology
  • Key AI terminology
  • Concept of ‘ground truth’
  • Conventional machine learning methods including parametric and non-parameter models
  • Bias-variance trade-offs in model development
  • Different forms of AI learning
  • Artificial neural networks
  • Radiomics, the current use of AI in medical image analysis, data analytics and data mining
  • AI in different subspecialties

Module 2 - Building AI: Key concepts

  • Preparing imaging data for AI development and data annotation
  • Information governance issues surrounding data sharing, data security and data privacy
  • Working with limited data
  • Building robust AI via collaboration and open datasets
  • AI grand challenges and their role in developing AI
  • Generalising AI models and their challenges
  • Role of algorithms to perpetuate or exacerbate existing biases in datasets and society
  • The importance of building bias-mitigating or bias-free systems

Online workshop content


Overview of online learning material

Discussion of use-cases and applications for AI in different areas of radiology including the basic terminology and development of AI algorithms with an opportunity to ask questions related to the topics from the online e-learning modules.

A primer to data curation and processing for machine learning in clinical radiology

A practical interactive workshop using Google Colab workbook to walk delegates through the basics of AI model building, using an exercise in Python.

Navigating the complexities of open-source datasets and AI grand challenges: Mitigating bias and unintended outcomes in AI models

Discuss the limitations and benefits of open-source datasets. What are grand challenges and what are the potential biases that impact implementation of such tools?

Addressing small data challenges in AI for radiology: Exploring the potential of GANs, transfer learning, and federated learning

How can we leverage small datasets or rare diseases to make AI models?



This workshop will be delivered using the Zoom, a link to join the event will be sent in your joining instructions 10-days before the event.


Registration type


Consultant Fellow and member






Once you have completed your registration, you will receive an email confirming your place. If you do not receive this email confirmation within 24 hours, please contact us at

Please note that although the content is the same, the workshop element of the Clinical radiology Artificial Intelligence (AI): A blended learning programme will be repeated three times, to offer delegates the choice of date and format that they wish to attend. The options are:

To book continue to book for Thursday 18 January please click “Event booking" below. To book another date option please use the hyperlink above to go the relevant event booking page.

Pre-requisites for attending the workshop

  • Completion of both online e-learning modules one and two before attending the workshop.
  • A laptop available (access for online workshop hosted on Zoom)
  • A Google account for running and accessing the Google Colab practical session

Knowledge from the modules will be assumed in the workshop - delegates will not maximise the learning opportunity from this workshop without having completed the two online modules beforehand.

CPD credits:

All participants will receive an RCR certificate for 12 CPD credits upon successful completion of all e-learning modules and a workshop.

Thanks to our other contributing authors:

  • Dr Amanda Isaac, Musculoskeletal Diagnostic and Interventional Consultant, Guy’s and St Thomas’ NHS Foundation Trust
  • Dr Sarah Hickmann, Radiology Registrar, Barts Health NHS Trust
  • Dr Kristofer Linton-Reid, Schmidt Futures AI in Science Research Fellow, Imperial College London
  • Dr Mathew Storey, Radiology Registrar, St George's Healthcare NHS Trust

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