Cambridge MedAI Seminar - March 2025

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• Dr Michail Mamalakis and Joshua Rothwell
• Thursday 27 March 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://www.eventbrite.co.uk/e/cambridge-medai-seminar-series-tickets-1301806461169?aff=oddtdtcreator

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 Thursday 27 March 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:

Explainable and Interpretable AI: Building Trust and Uncovering Patterns in Healthcare and Neuroscience – Dr Michail Mamalakis, Research Associate, Department of Psychiatry, University of Cambridge

Dr Michail Mamalakis is a research scientist at the University of Cambridge, specializing in AI, Machine Learning, Explainable AI and Computer Vision for biomedical applications. His work focuses on explainable AI (XAI) for integrating imaging, genomics, and phenotyping data in neuroscience and clinical decision-making. He has collaborated with leading institutions, including Oxford, Sheffield, and Cambridge, on projects in brain tumors, Alzheimer’s, cardiac arrhythmias and pulmonary hypertension. His research spans AI-driven biomarker discovery, uncertainty estimation, attributional interpretability in funtional and structural imaging and mechanistic interpretability in protein language models and large language models. Currently, he develops multi-modal AI frameworks for Alzheimer’s prediction and glioblastoma analysis contributing to high-impact projects like EBRAINS 2 .0.

Abstract: Explainability is a critical factor in enhancing the trustworthiness and acceptance of artificial intelligence (AI) in healthcare, where decisions have a direct impact on patient outcomes. Despite significant advancements in AI interpretability, clear guidelines on when and to what extent explanations are required in medical applications remain insufficient. In this talk, I will provide guidance on the need for explanations in AI applications within healthcare. I will discuss possible explainable AI frameworks that can be used to identify new patterns and offer insights through explainable AI methods. These approaches have the potential to uncover new biomarkers and novel patterns relevant to the applications of interest. Finally, I will present some basic examples from neuroscience research to illustrate these concepts.

Retrospective evaluation and comparison of state-of-the-art deep learning breast cancer risk prediction algorithms – Joshua Rothwell, PhD Student, Department of Radiology, University of Cambridge School of Clinical Medicine

Josh is an MBBS /PhD student, researching and evaluating commercial mammography AI tools for the detection and prediction of breast cancer.

Abstract: Breast ‘interval’ cancers present between screening examinations and have poorer prognoses compared to screen detected cancers. Risk prediction tools can identify women that are at increased risk of developing cancer, and may therefore benefit from supplemental imaging or increased frequency screening, to detect cancers earlier and improve patient outcomes.This talk focuses on the retrospective evaluation of two state-of-the-art deep learning risk prediction algorithms, attempting to quantify potential cancer detection rates if implemented into the NHS Breast Screening Programme and discern the characteristics of misclassified cancers.

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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