In a previous effort, we developed a hybrid R Shiny application combining deterministic clinical visualizations with LLM-assisted exploration for early-phase oncology data review. This talk presents an alternative delivery mode we're exploring — an interactive, automated Quarto report generated and delivered daily to end users. The workflow, orchestrated through Posit Connect, queries updated study data, produces an abridged set of key outputs, applies LLM-assisted interpretation to static visualizations, and incorporates a lightweight RAG pipeline (via ragnar) to ground AI-generated insights in study context. We'll share the setup and practical lessons from this early-stage effort. The Shiny app remains primary, but this explores a scheduled, push-based complement for users whose needs are better met by a delivered report than an open-ended tool.
Matthew Kumar
Bayer
Scheduled Clinical Insights: AI-Assisted Quarto Reporting for Oncology Studies
In a previous effort, we developed a hybrid R Shiny application combining deterministic clinical visualizations with LLM-assisted exploration for early-phase oncology data review. This talk presents an alternative delivery mode we're exploring — an interactive, automated Quarto report generated and delivered daily to end users. The workflow, orchestrated through Posit Connect, queries updated study data, produces an abridged set of key outputs, applies LLM-assisted interpretation to static visualizations, and incorporates a lightweight RAG pipeline (via ragnar) to ground AI-generated insights in study context. We'll share the setup and practical lessons from this early-stage effort. The Shiny app remains primary, but this explores a scheduled, push-based complement for users whose needs are better met by a delivered report than an open-ended tool.