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.
Event overview
We are thrilled to announce the return of our new blended learning programme combining the experience of virtual peer-to-peer learning in our interactive workshop with online self-paced e-learning material for a seamless and complementary flow of learning.
This course is designed for radiologists and allied healthcare professionals. Whether you’re just starting your AI journey or have some level of experience, this course is for anyone looking to gain a global overview of Artificial Intelligence (AI) in radiology and what it means for the speciality.
Ahead of the workshop, participants will receive access to two interactive e-learning modules. Each module offers 2-3 hours of in-depth learning. Then elevate your knowledge and network with peers at our exclusive workshop on 17 January 2025, led by industry leading AI experts in the field of radiology.
12 CPD points will be awarded on completion of the e-learning modules and 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:
- Gain an understanding of the fundamental principles that form the basis of AI
- Be able to 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?
- Clinical radiology consultants
- Clinical radiology trainees
- Allied healthcare professionals
This course is for healthcare clinicians at any level of training or experience who are working in the UK and overseas.
Please refer to the eligibility criteria below, if you’re still unsure which rate you’re eligible for please contact - [email protected]
RCR Members Consultant and Fellows- Consultants with an active RCR membership.
RCR Member Trainee- Trainees with an active RCR membership.
Non-Member Consultants - Consultants who don't have an active RCR
membership.
Non-Members Trainee & AHP- Trainees and allied healthcare professionals who on't have an active RCR membership. This includes radiographers, physicists, SHOs, SAS doctors and medical students.
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 |
Introduction Dr Tristan Barrett |
|
09:40
|
Overview of Online Learning Material - Modules 1 & 2 Dr Kieran Zucker & Dr Sameer Gangoli 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. |
|
11:45 |
Comfort break |
|
12:00
|
Addressing Small Data Challenges in AI for Radiology: Exploring the Potential of GANs, Transfer Learning, and Federated Learning Dr Arsany Hakim How can we leverage small datasets or rare diseases to make AI models? |
|
13:00
|
Navigating the Complexities of Open-Source Datasets: Mitigating Bias and Unintended Outcomes in AI Models Prof Janani Pillai & Dr Ahmed Maiter Discuss the limitations and benefits of open-source datasets. What are grand challenges and what biases happen when we implement tools designed this way? |
|
14:00 |
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.
“Excellent talks by all facilitators”
“Course aimed at the right level. Very comprehensive lectures. Easy to follow and understand”
Partner with the RCR
A partnership with the RCR offers your company the opportunity to form a mutually beneficial relationship, built on collaboration and connection. The RCR offers a variety of ways to get involved, including our new Annual Partnership packages, one and two-day events and our e-learning resources.
- Be a valuable part of the RCR’s growing community
- Network and engage face-to-face and online with multidisciplinary audiences
- Share details of new products and services
- Support and invest in the RCR’s mission: supporting excellence in medical imaging and cancer treatment.
If you are interested in partnering with us at this event or future events, please contact:
Catherine Bratcher
Head of Learning
+44 20 3805 4053
[email protected]