The pharmaceutical industry is shifting from traditional SAS-based workflows toward the open-source R ecosystem. R Shiny applications have become a popular solution for visualizing tables, figures, and listings. However, these applications often require a strong understanding of data structures and familiarity with interface components such as dropdowns, which may not be intuitive for clinical reviewers. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have opened new possibilities for how users interact with clinical data. In this session, we present an innovative R Shiny application, {DataChat}, that enables users to "chat with data" through a conversational interface. Powered by the {ellmer}, {shinychat}, and {ragnar} packages, along with internal statistical tools and utilities, the app integrates retrieval-augmented generation (RAG) capabilities tailored to the pharmaceutical domain. The solution emphasizes user-friendliness, enabling non-programmers and clinicians to explore datasets and derive insights while maintaining compliance with data privacy requirements and addressing concerns around statistical validity. This approach exemplifies the potential of AI-augmented tools to enhance clinical data review and exploration in a practical and accessible way.
Zhen Wu | Peng Zhang
CIMS Global | CIMS Global
Integrating LLM using R Shiny for Clinical Data Review by Ensuring Data Privacy and Validity
The pharmaceutical industry is shifting from traditional SAS-based workflows toward the open-source R ecosystem. R Shiny applications have become a popular solution for visualizing tables, figures, and listings. However, these applications often require a strong understanding of data structures and familiarity with interface components such as dropdowns, which may not be intuitive for clinical reviewers. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have opened new possibilities for how users interact with clinical data. In this session, we present an innovative R Shiny application,  DataChat , that enables users to "chat with data" through a conversational interface. Powered by the  ellmer ,  shinychat , and  ragnar  packages, along with internal statistical tools and utilities, the app integrates retrieval-augmented generation (RAG) capabilities tailored to the pharmaceutical domain. The solution emphasizes user-friendliness, enabling non-programmers and clinicians to explore datasets and derive insights while maintaining compliance with data privacy requirements and addressing concerns around statistical validity. This approach exemplifies the potential of AI-augmented tools to enhance clinical data review and exploration in a practical and accessible way.