Theatre 9: AI & Machine Learning

Tuesday 26th March

Wednesday 27th March


11.00 - 11.30

Dr Matthew Prime

Personalized healthcare: Leveraging data to improve patient care through clinical decision support in oncology

Roche, known for its biopharmaceuticals and in-vitro diagnostics solutions has entered the field of Clinical Decision Support by building a portfolio of software products. Learn how the NAVIFY ® Decision Support portfolio leverages excellence in clinical workflow, advanced analytics and innovative strategic partnerships to bring together diagnostic, real-world data to deliver personalized healthcare.


11.45 - 12.15

Dr Farzana Rahman

The Ethics of AI in Healthcare

Artificial intelligence has the potential to change the way we treat patients but the ethical implications are complex. This lecture will highlight some of the key areas that the clinical community needs to think about.


12.30 - 13.00

Dr Anthony Holmes

Bridging the gap: how good ideas become great products

Artificial Intelligence is everywhere, or appears to be. Yet whilst it has the potential to make a big difference in healthcare, in truth there are still relatively few genuine AI deployments that genuinely impact patient outcomes. Based on two decades of experience commercialising AI in healthcare, Anthony will provide some ‘do’s and don’ts’ to consider when bringing AI algorithms to market, including the most important: “Get out of the building!”.


13.15 - 13.45

Dr Ozan Oktay

Neural Networks in Medical Imaging: Towards More Reliable and Automated Clinical Analysis

In this talk, I will present the research projects that we have been working at Imperial College London and HeartFlow Inc. The presentation will mainly focus on applications of machine learning methodologies in medical image analysis tasks, including cardiac CT/MR image reconstruction, image quality enhancement, and image segmentation. In particular, I will present a few use cases of deep neural networks in clinical workflows, which are utilised to automate quantitative measurements and leverage the knowledge learnt from large annotated medical datasets.


14.00 - 14.30

Dr Hugh Harvey

Deep Learning in Breast Cancer Screening

An industry prospective of the challenges and pitfalls in developing and deploying deep learning algorithms within the NHS breast cancer screening programme.


14.45 - 15.15

Dr Matthew Fenech

Regulation of AI in healthcare: what should we expect?

AI hold tremendous promise for healthcare. Technologists frequently talk about ‘disrupting’ healthcare in the same way that technology has disrupted retail, communication, entertainment, and many other spheres of life. We are now seeing a culture clash, between the ‘move fast’ world of technology, and the ‘safety first’ world of medicine. What is the role of regulation in striking the right balance? What’s on the horizon? What should we, as patients and as clinicians, expect?


15.30 - 16.00

Dr Mark Halling-Brown

Practicalities of creating medical image research databases for AI

Large-scale, properly curated and live databases are essential for the creation and validation of artificial intelligent-enabled systems such as Computer Aided Detection/Diagnosis. It is not only important to ensure visibility of datasets used for training and testing, but also vital that validation of emerging AI-systems is possible utilising independent validation sets. This seminar will discuss the practicalities of forming such large-scale, drawing upon experience gained forming the OPTIMAM image database.


16.15 - 16.45

Piotr Giedziun & Michał Krasoń

Deep learning in oncology diagnostics

Medical diagnosis is a complex and time-consuming process. Facilitating it could bring us a number of benefits, e.g. increase the chances of early detection of cancer. During the seminar we will see how current state-of-the-art computer vision algorithms can be used to achieve this goal.