Welcome address Dr Amrita Kumar - Consultant Radiologist, Frimley Health NHS Foundation Trust, UK
09:50
Education and training in AI Dr Rhidian Bramley - Radiology Clinical Director, Christie NHS Trust, UK
10:10
Starting out in AI research Dr Mark Halling-Brown - Consultant Clinical Scientist, University of Surrey, UK
We will explore the skillsets, prerequisites, datasets, governance, study design considerations and ways to collaborate to enable a clinician or healthcare institute to start out in the world of AI research. We will briefly discuss AI research in the context of development of AI, however the focus will be on evaluation of AI in a healthcare setting..
10:30
AI: the French perspective Professor Isabelle Thomassin-Naggara, France
10:15
Panel discussion Chaired by Dr Amrita Kumar, featuring:Dr Rhidian Bramley; Dr Mark Halling-Brown and Professor Isabelle thomassin-Naggara
11:15
Session ends
Programme subject to change
To discover more about our speakers participating at our AI Global Conference click here.
Amrita has been recently named 2022 Top 50 Innovator and 2023 Top 60 Influential Woman in the UK for leading innovation in the use of AI within the NHS. She was appointed as a substantive Consultant Radiologist at Frimley Health in 2013 with a subspecialist interest in breast cancer screening. She has a keen interest in Artificial Intelligence and has set up trust wide research collaboration to implement a digital enabled AI infrastructure, as well as implement novel AI software for improved detection of breast and lung cancer.
Ultimately, her aim is to have a positive social impact on cancer screening within the NHS integrated with AI, working in conjunction with various stakeholders with a mission statement of patient and value-focused healthcare. She has also been appointed Chair at the British Institute of Radiology National Clinical Intelligence & Informatics Committee to look at the national integration and implementation of AI into clinical practice.
Dr Rhidian Bramley specialises in Oncological Imaging, Healthcare Management and Informatics and currently works as a Consultant Radiologist at the Christie NHS Trust, as Clinical Lead for Diagnostics, Digital and Innovation for the Greater Manchester Cancer Alliance, and as Clinical Lead for the Greater Manchester PACS programme.
As a former Associate Medical Director, Clinical Director of Radiology and Chief Clinical Information Officer (CCIO) at the Christie, and National Clinical Advisor for PACS in the National programme for IT, Connecting for Health and NHS England, and advisor to the national NHS Wales Informatics Service and Digital Healthcare Wales.
Professor Mark Halling-Brown is a consultant clinical scientist who runs a Scientific Computing team (SciCom) at the Royal Surrey NHS Foundation Trust. His background is in bioinformatics, computational immunology, and computer sciences. More recently he has worked in Medical Physics, focussing on the safe development and adoption of healthcare software. His team lead or participate in over 28 research projects and databases covering a variety of areas of healthcare research.
The SciCom team have considerable experience in development, validation and deployment of software tools and techniques across the NHS. The team works extensively with industry to provide data for training and independent validation of AI products and supports deployment of commercial tools into UK screening services. The best example of datasets the team have developed is the OPTIMAM project, one of the world’s largest mammography image databases and has led to involvement in development of AI techniques to automate breast cancer detection and to estimate future cancer risk. The database has over 8.5 million images and is continuing to grow into a world class tool for stimulating research.
They are experienced in large-scale image/data collection, storage and sharing of research data. The current focus of his team's work is on the provisions of correct datasets for training and independent validation of AI tools for healthcare as well as the safe adoption and integration into healthcare settings. They are active in developing techniques and approaches required to establish TREs suitable and able to support deep-learning training and validation at scale.
