Building exploratory analysis dashboards for clinical trials traditionally requires considerable expertise, extensive time, and deep familiarity with specialized frameworks such as R/Shiny and {teal}. In this talk, we share our experience exploring how Generative AI can significantly streamline this process. We will present a tool we developed, powered by Claude Code, that enables biostatisticians and clinical researchers to effortlessly create and immediately preview R/Shiny applications tailored for exploratory clinical data analysis using {teal}.
Throughout our journey, we experimented with various GenAI-driven solutions, including code editors integrated with AI assistance tools (e.g., GitHub Copilot), and approaches leveraging MCP servers. Our current implementation is an interactive AI-powered chat interface designed to simplify the application-building experience, providing users with immediate application previews, and reducing both development complexity and the learning curve associated with the {teal} package.
We will demonstrate how GenAI-driven solutions can empower users with limited programming backgrounds to rapidly build effective exploratory analysis dashboards, discuss challenges encountered, and highlight insights gained through our iterative development process.
Olga Mierzwa-Sulima | Jakub Nowicki
Appsilon | Appsilon
Interactive and Instant: GenAI-driven Creation of Clinical Trial Applications
Building exploratory analysis dashboards for clinical trials traditionally requires considerable expertise, extensive time, and deep familiarity with specialized frameworks such as R/Shiny and  teal . In this talk, we share our experience exploring how Generative AI can significantly streamline this process. We will present a tool we developed, powered by Claude Code, that enables biostatisticians and clinical researchers to effortlessly create and immediately preview R/Shiny applications tailored for exploratory clinical data analysis using  teal . Throughout our journey, we experimented with various GenAI-driven solutions, including code editors integrated with AI assistance tools (e.g., GitHub Copilot), and approaches leveraging MCP servers. Our current implementation is an interactive AI-powered chat interface designed to simplify the application-building experience, providing users with immediate application previews, and reducing both development complexity and the learning curve associated with the  teal  package. We will demonstrate how GenAI-driven solutions can empower users with limited programming backgrounds to rapidly build effective exploratory analysis dashboards, discuss challenges encountered, and highlight insights gained through our iterative development process.