Health Statistics in the Age of ML  & AI (Part 2/3)

Health Statistics in the Age of ML & AI (Part 2/3)

Our second guest speaker is Dr Heather Mattie. She is an expert working at the intersection between biostatistics, data and network science.

By MRC CEH Researchers' Society

Date and time

Thu, 21 Oct 2021 09:00 - 10:00 PDT

Location

Online

About this event

*************************************************************************************************************Please note: This event will be held online as well as in person. Due to the on-going pandemic, there will be limited capacity. Please email either a.gaines20@imperial.ac.uk or j.levermore@imperial.ac.uk if you wish to attend in person.

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Artificial intelligence (AI) holds great promise for improving both personal and public health, but only if ethics and human rights are central to its design and deployment. AI has been shown to aid diagnosis and prognosis of disease and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.

Though, many of our researchers in the centre have played a pivotal role in the development and usage of such models, with a variety of intended purposes ranging from vehicle detection in satellite imagery through to determining the aesthetic appeal of our urban environments. In response to ECR’s demand for more Machine Learning and AI training, the Researcher’s Society have organised the ‘Health in the Age of Machine Learning and Artificial Intelligence’ workshop featuring guest speakers from King’s College London, Harvard University, and Google Health. This seminar series is scheduled to run on Thursday, October 14th, 21st, and 28th from 17:00 until 18:00 and will be a blended learning experience, with limited spaces being available in-person (max = 15 persons) in the Michael Uren Building at White City Campus (room 1205), and remaining online via Teams.

In this seminar series guest speakers will discuss the use of novel data sources (geospatial location) for disease modelling, ethical design of AI and ML algorithms, and its implementation in a cancer diagnosis setting.

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