Tue, 09 May|
Turing Clinical AI x Statistical Methods for Health Equity Webinar: Dr Honghan Wu (UCL)
Dr Honghan Wu (UCL) will discuss bias in health data and AI in medicine, its impact on health inequality, and present a fairness definition, quantification framework, and experiment results using real-world Intensive Care Unit (ICU) datasets.
Time & Location
09 May, 12:00 – 13:00
About the event
The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.
Join us for our first ever collab webinar! With the Clinical AI interest group at the Alan Turing Institute, we're delighted to welcome Dr. Honghan Wu from UCL who will be joining us to explore the topic of '"Fair" in the context of AI-enabled clinical decision making'. You can find more information (including how to join) about the Clinical AI interest group here: Clinical AI | The Alan Turing Institute.
Dr Honghan Wu (UCL) will discuss bias in health data and AI in medicine, and its impact on health inequality without effective mitigations. He will then initiate a discussion on what is fairness and present a definition for fairness, a quantification framework, and experiment results using real-world Intensive Care Unit (ICU) datasets.
Dr Honghan Wu is an Associate Professor in Health Informatics and a fellow at The Alan Turing Institute. He has worked at various universities, including the University of Aberdeen, King's College London, and the University of Edinburgh, and he still holds visiting research positions at the latter two. His research was funded by Health Data Research UK for his UKRI Rutherford fellowship while working at the Centre for Medical Informatics, University of Edinburgh, and the Institute of Health Informatics, UCL. Dr Honghan Wu completed his PhD in Computing Sciences from Southeast University, China. Currently, he is leading the Health Informatics group called KnowLab and co-leading the Clinical NLP group at the University of Edinburgh. His current research interest is in using text technologies and Knowledge Graph techniques to analyze health data, combining data-driven machine learning approaches with knowledge-driven methods. He also works with NHS Trusts and health boards across the UK to use artificial intelligence technologies in research and health service improvement. Additionally, he serves as an editorial board member for BMC Medical Informatics and Decision Making and Scientific Reports journals.
Please direct any questions about the webinar series to Dr Brieuc Lehmann at email@example.com.