Introduction
As part of the Turing's Clinical AI Interest Group, we have formed supra-interest groups in different clinical specialties to bring together clinicians and AI experts in a more focused manner - see webpage for details of all the groups.
The current supra-interest groups are:
- Anaesthetics and Intensive Care
- Medical imaging and computer vision
- AI for Women's Health
- Pathology
- Public Health
- NeuroAI
- MSK AI
About the event
This event is the eight meeting of the AI for Women's Health Group. This group is being led by Bianca Schor, Kristin Collett Caolo and Annalisa Occhipinti with additional help from the organisers of the Clinical AI Interest Group.
The AI for Women's Health Group is an interdisciplinary community (e.g. clinicians, academics, data scientists, designers, health care professionals, students etc) interested in advancing women's health and how to leverage AI to do so.
The groups goals include:
- Building an interdisciplinary and diverse community of individuals working or interested to work in women's health with (or without) AI.
- Bringing visibility to research on women's health and how AI can help to do so.
- Facilitating collaborations and bringing more people to the field
- Offering relevant events & trainings, and sharing resources with the community (mostly online for now).
In this meeting, we will have talks from:
Speaker: Dr Huiqi Yvonne Lu, University of Oxford.
Title: Clinical AI and remote monitoring for women with gestational diabetes
Bio:
Dr Huiqi Yvonne Lu is a clinical machine learning scientist and a Stipendiary Lecturer in Engineering Science at the University of Oxford. She also holds an honorary Research Fellow position at the George Institute for Global Health, Imperial College London. Her research focuses on clinical machine learning, sensor signal processing, and wearable devices for patient monitoring, especially on digital health innovations for global women’s health and chronic health conditions such as diabetes. Her current research interest is to develop health foundation model for time-series data, and exploring the feasibility of using meta-learning with large language models for explainable AI for health monitoring, disease discovery, thereby reduce digital health disparities, especially for LMICs. One of her recent research adventures is to develop reasoning-informed model to enhance clinical capacity in India using large language models, funded by the Bills and Melinda Gates Foundation Global Challenge Grant and the George Institute for Global Health.
She is an Associate Editor of Nature npj Women’s Health and a Co-Chief Editor of the special collection of Advances in AI for women’s health, reproductive health, and maternal care: bridging innovation and healthcare. She has served as a workshop committee member and junior round table chair at at notable conferences, including ICLR (PMLDC), NeurIPs (ML4H), IJCAI(KDHD), and the PHME. Dr Lu is an active contributor in the IEEE Standard Committee for P3191: Performance Monitoring of Machine Learning-enabled Medical Device in Clinical Use.
Speaker: Dr Rahele Kafieh, Durham University
Title: Empowering Women's Health: AI-Driven Early Detection of Multiple Sclerosis through Eye Imaging
Bio:
Rahele Kafieh is an Assistant Professor of Bioengineering and serves as an EPSRC Women in Engineering Ambassador. Her research uses Artificial Intelligence (AI) for biomedical image and signal processing, with a focus on early disease diagnosis, home-based imaging solutions for elderly populations, and AI-driven referral systems connecting community care to hospitals. She has over 12 years of expertise in medical data processing with AI, contributing to more than 150 research publications in this field. Rahele has secured several prestigious grants, including funding from the Dunhill Medical Trust, EPSRC IAA, UKRI MRC Ageing Research Development Award, Switzerland Research Seed Money, Einstein Forum, National Institute for Medical Research Development, and TÜBİTAK.
Register now
Register using this link: https://turing-uk.zoom.us/meeting/register/urzmrV09TFGuIBp9SxPPAA