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Stream 5, Session 1: Clinical radiology AI taster session

The morning session will provide a foundational overview of deep learning principles and practical approaches to address data bias, a critical concern in model reliability and fairness. Deep learning concepts are demonstrated in Session 1. By the end of session 1 participants will understand neural networks, convolutional layers, and the role of large datasets in training accurate models. Session 2 is dedicated to exploring bias in data - where it originates from, how it manifests in models, and the consequences for real-world applications. Participants will learn techniques to identify and mitigate bias. Real-world examples will show how these techniques reduce skewed predictions and improve model generalisability.

Interactive discussions will ensure effective learning, and help participants to fully understand essential concepts.

Time

Session details

09:45

Welcome and introductions 
Dr Thomas Booth, CRAI Faculty Lead, Kings College London and Kings College Hospital, UK

10:00 

Taster session 1 - Deep learning explained - the essentials 
Dr Matthew Townend, Kings College London and North West School of Radiology, UK

10:30

Taster session 2 - Understanding data bias - the essentials 
Dr Sonam Vadera, University Hospitals of Leicester and University Hospitals of Birmingham, UK 

11:00

Q&A 
Dr Thomas Booth, CRAI Faculty Lead, Kings College London and Kings College Hospital, UK

11:15

Session ends

Programme subject to change

To discover more about our speakers participating at our AI Global Conference click here.

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Speakers

Dr Thomas Booth

Consultant Diagnostic and Interventional Neuro Radiologist and CRAI Faculty Lead
Kings College London and Kings College Hospital, UK
Dr Thomas Booth
  • Dr Thomas Booth

    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. 

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Dr Matthew Townend

Clinical Research Fellow
Kings College London and North West School of Radiology, UK 
Dr Matthew Townend
  • Dr Matthew Townend

    Matt is a radiology resident doctor working in the Mersey deanery, with a part-time Clinical Research Fellow post at King's College London. He has an interest in neuroimaging and machine learning, and experience with software and artificial intelligence development within industry and academia. His clinical research software, for automated retrieval and deidentification of imaging data, is in use at several NHS sites.

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Dr Sonam Vadera

Radiologist and Clinical AI Fellow
University Hospitals of Leicester and University Hospitals of Birmingham, UK
Dr Sonam Vadera
  • Dr Sonam Vadera

    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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