SIG-HLTH is seeking proposals for the ASIS&T SIG-HLTH Workshop, Beyond Prompting - Generative AI Workflows in Health Research and Practice.

We invite proposals for interactive workshop sessions that demonstrate practical applications of generative AI in health research and practice. We welcome contributions from researchers, practitioners, and interdisciplinary teams that illustrate scalable, research-grade workflows using generative AI. Proposals may be based on recently published work or papers accepted for presentation at ASIS&T 2026 or other conferences, provided they are adapted for an educational and interactive workshop rather than a traditional research presentation. The goal is to share practical methods, lessons learned, and transferable workflows that participants can apply in their own research and professional practice.

We welcome submissions from researchers, practitioners, clinicians, students, educators, and first-time ASIS&T presenters. This is a non-archival workshop, so proposals may be based on work that has already been published or accepted elsewhere. Contributors who want to share practical workflows, lessons learned, and hands-on activities and/or are interested in transforming their work into engaging, interactive learning sessions are welcome.

An 800 to 1,200-word proposal (excluding references) that aligns with or builds upon the workshop's learning outcomes should be submitted via the Google Form linked below. The proposal should consist of two components. First, presenters should provide a brief overview of the underlying research or project, including its motivation, methods, key outcomes, and implications for health informatics or related fields. Second, presenters should describe an interactive, hands-on learning activity that actively engages participants, such as a live demonstration, guided tutorial, workflow walkthrough, collaborative exercise, or facilitated discussion. Because the workshop will be conducted entirely online, the interactive component should be designed to be effective in a virtual environment and encourage active participation. Sessions should emphasize practical implementation of generative AI while addressing methodological considerations such as evaluation, transparency, bias, and responsible human-in-the-loop use.

We look forward to your submissions. If you have any questions, please contact the SIG-HLTH chair (Dr. Heejun Kim) at heejun.kim@unt.edu.

Sincerely,

ASIST SIG-HLTH Workshop Organizing Committee