Cambridge MedAI Seminar - June 2025

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• Pascal Wodtke and Alexander Nicholas Shepherd
• Wednesday 25 June 2025, 11:45-13:00
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.

If you have a question about this talk, please contact Hannah Clayton.

Sign up on Eventbrite: https://medai_june2025.eventbrite.co.uk

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s. This series brings together leading experts to explore cutting-edge AI applications in healthcare—from disease diagnosis to drug discovery. It’s a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month’s seminar will be held on Wednesday 25 June 2025, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. A light lunch from Aromi will be served from 11:45. The event will feature the following talks:

Phenotyping a rare case of GIST : How to image metabolism using MRI – Pascal Wodtke, PhD student, Department of Radiology, University of Cambridge

Pascal is a biomedical physicist in the department of Radiology and Gates Cambridge Scholar, focussing on the development and application of novel non-invasive MRI techniques for imaging metabolism. For his development of the pH sensor Z-OMPD he was selected as a runner-up for the Young Investigator Award of the European Society for Molecular Imaging and most recently he won the Magna cum Laude award by the International Society for Magnetic Resonance in Medicine.

Abstract: Succinate dehydrogenase-deficient (SDHd) gastrointestinal stromal tumors (GIST) are a rare type of cancer affecting mostly young adults.The genetic defect causes altered metabolism. Hyperpolarized magnetic resonance spectroscopic imaging (MRSI) is a novel imaging technique that enables quick, non-invasive imaging of metabolic features. Since conventional GIST treatments don’t work for this type of cancer, accurate diagnosis is crucial, and more research needs to be performed in these patients.This talk presents a way to image the metabolic hallmarks of SDHd GIST using a tailored MRSI protocol.

Shaping Responsible AI in Health Technologies – Alexander Nicholas Shepherd, AI Client Manager, British Standards Institute

Alex Shepherd is an AI auditor at the British Standards Institute, working on evaluating the effectiveness of Responsible AI Management in organisations. He has previously worked as a Data Scientist in the Healthcare and Life Science sectors and working towards being an “enlightened data scientist”: to understand the benefits and risks of AI technologies that are developed, provisioned and used. He is currently writing a book to guide AI professionals on implementing Responsible AI in their organisations.

Abstract: The objective of this presentation is to provide a concise overview of the Responsible AI framework, conceptualized as a “pyramid” comprising legal, ethical, technical, and standards-based foundations.This layered structure serves to support an ecosystem of innovation, regulatory compliance, and trust within organizations deploying AI technologies. The talk will also explore key AI ethics principles, examining their role and alignment within each layer of the Responsible AI pyramid, with particular emphasis on their implications for the development and application of AI in medical and healthcare contexts.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

This talk is part of the Cambridge MedAI Seminar Series series.

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