Queen Mary University of London
Wolfson Institute of Preventive Medicine
Fiona Walter is Professor of Primary Care Cancer Research at Barts and The London School of Medicine and Dentistry, Queen Mary University of London, UK, where she is also Director of the Wolfson Institute of Preventive Medicine and the Institute of Population Health Sciences. Fiona is also Honorary Senior Visiting Fellow at The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, UK. Her research group delivers world-class research focusing on the cancer pathway, from prevention, screening, early detection and diagnostics to post-treatment care; she also focus on translating genetics advances into primary care. Fiona co-leads the international, multi-institutional CanTest Collaborative, Cancer Research UK’s first Catalyst award, aiming to accelerate progress towards improving cancer outcomes by focussing on the transformative implementation in primary care of tests to support early detection of cancer. She is an inaugural member of Cancer Research UK’s Early Detection & Diagnosis Committee. Her international work is marked by an honorary academic role at the University of Melbourne, Australia, where she contributes to parallel programmes of work on improving outcomes for cancer patients. She also co-leads research in Southern Africa and contributes to research in Europe and the US.

Presenter of 1 Presentation

ARTIFICIAL INTELLIGENCE TECHNIQUES TO FACILITATE THE EARLIER DIAGNOSIS OF CANCER IN GENERAL PRACTICE

Date
09.07.2021, Friday
Session Time
10:30 AM - 12:00 PM
Room
Hall 2
Lecture Time
11:04 AM - 11:21 AM
Session Icon
Pure Live

Abstract

Abstract Body

There is accumulating evidence that artificial intelligence (AI) can assist clinicians to make better clinical decisions, or even replace human judgement, in certain areas of healthcare. This is due to the increasing availability of healthcare data and rapid development of big data analytic methods. There has been increasing interest in the application of AI in medical diagnosis, including machine learning and automated analysis approaches. This talk will present results from two recent reviews. One examines the application of AI techniques to primary care electronic healthcare data. The second examines whether AI/machine learning algorithms which facilitate early detection of skin cancer are accurate and safe enough for use in community and primary care settings.

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