AI in Public Health: Responsible and productive use of new technologies

About

Artificial intelligence technologies such as large language models are evolving at a rapid pace. They will likely shape research and practice of Public Health and Epidemiology over the next few years. How can we align the use of such technologies with established principles from Epidemiology and Public Health? What criteria define responsible and impactful use of these new tools?

Our course will cover these and related questions with a pragmatic, hands-on approach. We will review the traditional paradigms of Epidemiology and Public Health to examine how they align with novel approaches arising from Data Science. We will try out and scrutinize a selection of generative AI tools for data collection and processing to examine their usefulness, accuracy, and unbiasedness. Specifically, we will explore the use of large language models – as well as their limitations and ethical considerations - for specific public health-related tasks, including finding and reviewing existing studies, as a writing support tool, and for data summarization. We will also learn how such tools can be used to enhance health, in low-resource settings as well as high resource settings.

The course will include input lectures and hands-on exercises using freely accessible tools where possible. Ultimately, this course aims to foster clear thinking about the responsible and productive use of new technologies in Public Health and Epidemiology. Participants will learn tools to critically reflect on how technology can serve their goals, rather than the other way around.

Course leader

Prof. Dr. Viktor von Wyl(University of Zurich); Prof. Dr. Janna Hastings (Idiap Research Institute)

Target group

Practitioners, researchers, and graduate students

Course aim

By the end of the course participants will:
- Have an understanding of how to align digital methods with core principles of Epidemiology and Public Health;
- Gain an understanding of the capabilities and limitations of large language models;
- Be able to critically reflect on the usefulness of novel technologies for Public Health and health care;
- Have acquired approaches to collaborate in multidisciplinary teams.

Fee info

Discounts are available for PhD and Master’s students, participants from UMICs and LMICs, and members of LSS partner institutions (USI, SUPSI, Swiss TPH, and SSPH+). For details on fees and available discounts, contact us at info@ssph-lugano-summerschool.ch or visit our website.