Dr Matthew Fenech
Dr Matthew Fenech, MD PhD is an Affiliate Consultant at Future Advocacy, an independent think tank focused on developing policies that maximise the opportunities and minimise the risks of AI. His main interest is in the ethics and practicalities of the use of AI in health and medical research, a field to which he brings his 10 years of experience working as a clinical academic. He was the lead author of a report for the Wellcome Trust on ‘Ethical, Social, and Political Challenges of AI in Health’, as well as writing a chapter for the Chief Medical Officer’s Annual Report 2018. He is now working on writing guidance for NHS England on the validation of algorithms for use in healthcare. He has also authored reports about AI-driven business models, and on the impact of automation on the future of work. He regularly delivers lectures and appears in the media, speaking about these topics.
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?
EVEN MORE SEMINARS
Dr Mark Halling-Brown Royal Surrey County Hospital
Practicalities of creating medical image research databases for AI
Prof Karen J Kirkby The University of Manchester / The Christie NHS Foundation Trust
Proton Beam Therapy in Manchester the story so far
Pamela Black Wirral University Teaching Hospitals
Improving Radiology Services: A local perspective
Jane Mackewn King’s College London and Guy’s and St Thomas’ PET Centre
Practical Experience of using the mMR for Clinical Research Studies
Dr Sue Astley University of Manchester
Deep learning for breast density assessment and risk prediction