MENU e-HAIL Symposium 2024
Join Us!
The 3rd annual e-Health and Artificial Intelligence (e-HAIL) symposium, Generative AI in Healthcare, is coming soon!
Accelerating interdisciplinary collaborations through opportunities for engagement around AI & health research is a major focus of the e-HAIL initiative, and the symposium is set up to maximize learning from and connecting with fellow faculty researchers interested/working in this field.
Friday, September 13
10:00 AM – 4:00 PM
North Campus Research Complex
Building 18, Dining Hall
2800 Plymouth Rd
Ann Arbor, MI
(RSVPs are required for in-person attendance)
Agenda
9:45
Coffe and registration
10:00
Opening remarks
Julie Lumeng, M.D., Associate Dean for Clinical Research for the Medical School, Associate Vice President for Clinical and Human Subjects Research
10:10
Keynote 1 (followed by audience Q&A)
Language Models As Temporary Training Wheels to Improve Mental Health
Tim Althoff, M.S., Ph.D., Assistant Professor, Computer Science, University of Washington
Tim is an assistant professor in the Allen School of Computer Science & Engineering at the University of Washington. Tim’s research seeks to better understand and empower people through data and computation. His AI research has directly improved mental health services utilized by over ten million people and informed federal policy. Tim holds a Ph.D. from the Computer Science Department at Stanford University. His work has received various awards including WWW, 2x ICWSM, ACL, UbiComp, and IMIA Best Paper Awards, the SIGKDD Dissertation Award 2019, and an NSF CAREER Award. Tim’s research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
Abstract: Access to mental health care falls short of meeting the significant need. More than one billion individuals are affected by mental health conditions, with the majority not receiving the necessary treatment. In this talk, Tim will describe how human-AI collaboration, critically enabled by language models, can improve access to and quality of mental health support. Language models have the potential to act as temporary training wheels providing immediate support and guidance to help individuals develop essential mental health skills. This approach emphasizes the importance of using these tools as initial aids rather than long-term crutches. By offering structured assistance, practice, and feedback, language models can help individuals and professionals learn skills, such as cognitive reframing, emotional regulation, and conflict resolution. However, the ultimate goal is for individuals to gradually transition away from dependence on these models, fostering sustained skill development and long-term well-being. This talk will describe how language models can be developed towards these aims and evaluate their effectiveness across multiple randomized trials and real-world deployments with over 150,000 participants.
11:10
Coffee break and Video Research Showcase
11:45
Keynote 2 (followed by audience Q&A)
Susan A. Murphy, Ph.D., Mallinckrodt Professor of Statistics and of Computer Science; Associate Faculty, Kempner Institute, Harvard University
Online Reinforcement Learning in Digital Health Interventions
Susan is the Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute, Harvard University. Her research focuses on improving sequential decision making via the development of online, real-time learning algorithms. Her lab is involved in multiple deployments of these algorithms in digital health. She is a member of the US National Academy of Sciences and of the US National Academy of Medicine. In 2013, she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She is a Fellow of the College on Problems in Drug Dependence, Past-President of Institute of Mathematical Statistics, Past-President of the Bernoulli Society, and a former editor of the Annals of Statistics.
Abstract: In this talk, Susan will discuss first solutions to some of the challenges we face in developing online RL algorithms for use in digital health interventions targeting patients struggling with health problems such as substance misuse, hypertension, and bone marrow transplantation. Digital health raises a number of challenges to the RL community including different sets of actions, each set intended to impact patients over a different time scale; the need to learn both within an implementation and between implementations of the RL algorithm; noisy environments; and a lack of mechanistic models. In all of these settings, the online line algorithm must be stable and autonomous. Despite these challenges, RL, with careful initialization, with careful management of bias/variance tradeoff, and by close collaboration with health scientists, can be successful. We can make an impact!
12:45
Poster presentations and lunch
1:45
Panel discussion – “Generative AI in Healthcare” (with audience Q&A)
Moderator:
Rada Mihalcea, PhD, Janice M. Jenkins Collegiate Professor of Computer Science and Engineering, Professor of Electrical Engineering and Computer Science
Panelists:
Dana Habers, M.P.H., Chief Innovation Officer, U-M Health & Chief Operating Officer, Pharmacy
JJ Park, Ph.D., Assistant Professor, Computer Science & Engineering
Mike Sjoding, M.D., Associate Professor, Internal Medicine, Division of Pulmonary & Critical Care
V.G. Vinod Vydiswaran, Ph.D., Associate Professor, Department of Learning Health Sciences
Jenna Wiens, Ph.D., e-HAIL Advisory Convener, Associate Professor, Electrical Engineering & Computer Science
3:00
Networking & resources fair
Questions?
Contact J. Henrike Florusbosch, Ph.D., e-HAIL Program Manager, at jflorusb@umich.edu.