Tristan Barrett is Associate Professor in Radiology at the University of Cambridge. He has previously completed fellowships in molecular imaging at National Institutes for Health (USA), and in abdominal imaging in Memorial Sloane Kettering Cancer Centre, New York and University of Toronto. He is the Clinical lead for urogenital radiology at Addenbrooke’s Hospital Cambridge and imaging lead for East Anglia cancer alliance. He has published over 150 research papers, with a personal H-index of 53 and over 10,00 citations. His research interests centre on characterising prostate tumours, including through novel MRI techniques, AI applications and X-nuclei MR imaging.
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Event overview
We are thrilled to announce the return of our AI blended learning programme. This unique programme combines the experience of virtual peer-to-peer learning in our interactive workshop with online self-paced e-learning modules for a seamless and complementary flow of learning.
The Clinical Radiology AI Blended Learning course Modules 1 and 2 are the first in a wider series being developed by RCR Learning and the Clinical Radiology AI Faculty. Whether you're new to AI or have some experience, this introductory course will guide you through the world of AI in radiology and healthcare. You’ll explore the fundamental concepts and gain a solid foundation in this rapidly evolving field.
Available in late 2025, you can further expand your knowledge with Modules 3 and 4.
Before the workshop, you’ll gain valuable foundational knowledge with two interactive e-learning modules. Each module provides 2-3 hours of in-depth learning at your own pace. Then, elevate your knowledge and network with peers at our exclusive online workshop on 16 September 2025, led by industry leading AI experts in the field of radiology.
12 CPD points will be awarded on completion of the full course. You will receive an automated certificate for 6 CPD credits once you have completed the e-learning, and a separate certificate for the remaining 6 CPD credits for attending the online workshop. Please note this event will not be available to view on-demand post event.
Learning objectives
You will be introduced to AI in radiology and healthcare and the fundamental concepts when creating an AI algorithm.
By the end of this course, you will be able to:
- Understand the fundamental principles that form the basis of AI
- Describe various AI techniques and their advantages and disadvantages, as well as justify the use of these methods
- Acquire basic knowledge of how AI models are created, with a particular focus on data gathering, annotation, and the importance of data management and security
- Understand the challenges associated with open-source datasets, be aware of AI grand challenges, and understand how bias and unintended outcomes can potentially affect AI models.
Who should attend?
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Clinical radiology consultants
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Clinical radiology trainees
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Allied healthcare professionals
This course is for healthcare clinicians at any level of training or experience who are working in the UK and overseas.
Registration fees
16 September 2025
Please refer to the eligibility criteria below, if you’re still unsure which rate you’re eligible for please contact - [email protected]
RCR consultant Fellows and members: Consultants with an active RCR membership.
RCR member trainee & non-member allied healthcare professionals (AHP): This applies to trainees with an active RCR membership, as well as AHPs, who will not have an RCR membership, including, radiographers, physicists, SHOs, SAS doctors and medical students.
Non-member consultants: Consultants who don't have an active RCR membership.
Non-member trainees: Trainees who don't have an active RCR membership.
Lower middle income country registration: A reduced registration fee is available for those who reside in and working in a lower or middle income country (LMIC) as defined by the World Bank. These rates do not apply if you are originally from one of these countries but are now practising elsewhere.
Pre-requisites for attending the workshop
- Completion of both online e-learning modules one and two before attending the workshop.
- A laptop available for accessing the online workshop.
- A Google account for running and accessing the Google Collab 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.
Programme
|
Timing |
Topic |
|
09:30 |
Introductions Dr Tristan Barrett |
|
09:40
|
Overview of online learning material - Modules 1 & 2 Discussion of use cases and applications for AI for different areas of radiology including the basic jargon and development of AI algorithms with opportunity to ask questions related to the topics from the online e-learning modules. Dr Kieran Zucker, Dr Sameer Gangoli |
|
11:45 |
Comfort break |
|
12:15
|
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? Dr Arsany Hakim |
|
13:15
|
Navigating the complexities of open-source datasets: Mitigating bias and unintended outcomes in AI models Discuss the limitations and benefits of open-source datasets. What are grand challenges and what biases happen when we implement tools designed this way? Dr Iosif Alexandru Mendichovszky |
|
14:15 |
Comfort break |
|
14:30 |
A primer to data curation and processing for machine learning in clinical radiology Dr Ahmed Maiter |
|
15:00 |
Summary and Q&As Dr Tristan Barrett |
|
15:15 |
Event close |
Speaker faculty
11
Location
This workshop will be delivered via Zoom, a link to join the event will be sent in your joining instructions one week before the event.
Access to the e-learning modules will be sent to delegates ahead of the event.
“Lectures were excellent with very useful examples to illustrate challenging concepts.”
“Well informed speakers. Very informative course.”
“Interesting and informative course. Good pre-course material. Well done.”
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