Mon, 16 Oct|
Statistical Methods for Health Equity Webinar: Karandeep Singh (University of Michigan)
This webinar talk explores healthcare AI implementation failures, addressing model and intervention issues. Real examples discussed will illustrate challenges, emphasising the need for anticipatory design to enhance patient care.
Time & Location
16 Oct, 15:00 – 16:00
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.
After the summer break, we are delighted to inform you that we are resuming our webinar series by welcoming Dr Karandeep Singh from University of Michigan to give a talk at our upcoming webinar.
Dr Karandeep will be joining us to explore some discussions around common pitfalls in Health AI implementation, covering model and intervention-related challenges, emphasising on plausible solutions to improve patient care outcomes.
Specific details on the topic are as below:
Why Health AI Implementations Fail
In this talk, Dr Karandeep will use real-world examples to illustrate common implementation roadblocks that can lead AI models to fail in the context of healthcare. AI model implementations can fail for many reasons, including issues related to models (reproducibility, transportability, net benefit, modifiable vs. non-modifiable risk) and issues related to interventions (lack of efficacy, wrong end-users, increasing workload, readiness for change, and resource constraints). In the talk, we will discuss how we can anticipate these issues, estimate their impact, and better design interventions to make a positive impact on patient care.
Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He directs the Machine Learning for Learning Health Systems (ML4LHS) Lab, which focuses on translational issues related to the implementation of machine learning models within health systems. He serves as an Associate Chief Medical Information Officer of Artificial Intelligence for Michigan Medicine and is the Associate Director for Implementation for Precision Health at the University of Michigan. He teaches a health data science course for graduate and doctoral students, and provides clinical care for people with kidney disease.
Please direct any questions about the webinar series to Dr Brieuc Lehmann at email@example.com.