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 seventh meeting of the AI for Women's Health Group. This group is being lead 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: Dilara Tank, Amsterdam University Medical Centre
Title: Deep Learning-Based Uterus Segmentation in Transvaginal Ultrasound: A Feasibility Study
Bio: Dilara is a PhD candidate in the Radiology department at Amsterdam UMC, where her research centers on leveraging deep learning techniques for quantitative MRI to develop personalized cancer treatments. She holds a BSc in Artificial Intelligence and an MSc in Medical Informatics, with a keen interest in computer vision, deep learning, and medical imaging. During her time as a visiting student in Cambridge, Dilara conducted her MSc thesis project on AI-driven automated segmentation in gynecological ultrasound.
Speaker: Stefanie Felsberger, University of Cambridge
Title: Cycle tracking and the 'normal' period
Bio: Dr. Stefanie Felsberger is a sociologist specialising in technology and gender. She approaches her research with a firm aim in countering the techno-solutionist ethos that buttress period apps and other technologies. She grounds her analysis in evaluating the effectiveness and potential impact of technology across varying sociological contexts.
Stefanie holds a PhD in Multi-Disciplinary Gender Studies from the University of Cambridge. Her research asked how users of cycle tracking apps in Austria navigate the commodification of their menstrual data through a lens of Data Justice. She is currently a Research Associate at the Minderoo Centre for Technology and Democracy researching how users employ AI tools to counter disinformation.
Register now
Register using this link: https://turing-uk.zoom.us/meeting/register/tJckceisqzIuH9WgHaxGldwCAsL6N85pc17K
