12 CPD credits

Clinical Radiology Artificial Intelligence (AI): Blended learning course - FULLY BOOKED

10 Dec 2024

Event overview

Due to the overwhelming popularity of our December 2024 course, which is now fully booked, we're excited to announce that bookings are open for the 17 January 2025 Workshop.

Registration is now open. Early booking is highly recommended to secure your spot.

Book the January Workshop now

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.

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 Thomas Booth
09:40	Overview of Online Learning Material - Modules 1 & 2 Dr Matthew Townend & Dr Jonathan Nash 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 Mathew Storey 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 Dr Sonam Vadera 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 Liane Canas
15:00	Summary and Q&As Dr Thomas Booth
15:15	Event close

Speaker faculty

Tom is a Reader in Neuroimaging at King’s College London and an Honorary Consultant Neuroradiologist at King’s College Hospital. His research focuses on AI-driven neuro-oncology and abnormality detection in diagnostic imaging, and neurovascular robotics. His PhD at the University of Cambridge focused on brain tumor treatment response, a subject he continues to explore. Tom leads multiple UK multicentre studies, including a large AI-driven project on brain MRI abnormality detection. He sits on national and international committees and has received the Royal College of Radiologists' Outstanding Researcher Award.

I am a Research Fellow at the Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences (BMEIS) at King's College London (KCL), London, UK. My research is focused on the development of disease progression models to study infectious and neurodegenerative diseases using imaging and textual data, and the application of Digital Twins for the development of personalised prognosis models. Through my research, I aim to improve and translate newly developed models from computer vision and pattern recognition domains to healthcare problems.

Enthusiastic experienced NHSBSP breast radiologist now working with fantastic teams across the UK developing and delivering breast screening AI for global benefit. Main areas of interest are team working, quality, healthcare IT with data management and education. Keen to bring breast imaging to the next place.

Dr Sonam Vadera is a Radiologist, Clinical AI Fellow and a Member of the RCR Clinical AI Faculty. Her expertise lies in the safe integration of AI technologies into clinical workflows and AI safety monitoring, particularly in the post-market surveillance phase. She is involved in research that examines the regulation, clinical implementation, validation, and post-deployment performance of AI-enabled medical devices. She is also leading research on whether and how LLMs should be regulated as medical devices. Additionally, she is actively engaged in education, serving as an Editor for Radiopaedia, a leading global platform for radiology education.

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Tom is a Reader in Neuroimaging at King’s College London and an Honorary Consultant Neuroradiologist at King’s College Hospital. His research focuses on AI-driven neuro-oncology and abnormality detection in diagnostic imaging, and neurovascular robotics. His PhD at the University of Cambridge focused on brain tumor treatment response, a subject he continues to explore. Tom leads multiple UK multicentre studies, including a large AI-driven project on brain MRI abnormality detection. He sits on national and international committees and has received the Royal College of Radiologists' Outstanding Researcher Award.
I am a Research Fellow at the Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences (BMEIS) at King's College London (KCL), London, UK. My research is focused on the development of disease progression models to study infectious and neurodegenerative diseases using imaging and textual data, and the application of Digital Twins for the development of personalised prognosis models. Through my research, I aim to improve and translate newly developed models from computer vision and pattern recognition domains to healthcare problems.
Enthusiastic experienced NHSBSP breast radiologist now working with fantastic teams across the UK developing and delivering breast screening AI for global benefit. Main areas of interest are team working, quality, healthcare IT with data management and education. Keen to bring breast imaging to the next place.
Dr Sonam Vadera is a Radiologist, Clinical AI Fellow and a Member of the RCR Clinical AI Faculty. Her expertise lies in the safe integration of AI technologies into clinical workflows and AI safety monitoring, particularly in the post-market surveillance phase. She is involved in research that examines the regulation, clinical implementation, validation, and post-deployment performance of AI-enabled medical devices. She is also leading research on whether and how LLMs should be regulated as medical devices. Additionally, she is actively engaged in education, serving as an Editor for Radiopaedia, a leading global platform for radiology education.

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 in October 2024.

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“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]