Background:
Artificial intelligence is transforming drug development, but its integration into biostatistics and clinical workflows remains limited. Clinical and biometrics teams often face barriers in exploring high-dimensional, multi-view trial datasets, requiring technical expertise to extract insights and generate visualizations.
Methods:
We developed an AI-enhanced R Shiny application that integrates ellmer with ChatGPTs. The app connects to Cellular-Mediated Immunogenicity (CMI) data across multiple mRNA infectious disease programs. Natural language queries are translated into structured R operations, through which users can interactively explore and visualize data, eliminating the need for manual coding.
Results:
The application successfully handled unstructured user queries, generated targeted outputs, and produced customizable visualizations. Pilot deployment across infectious disease programs demonstrated faster extraction of trial insights, reduced dependency on ad-hoc programming
support, and invigorates collaboration between statisticians, clinicians, and translational researchers.
Conclusions:
Embedding AI agents via ellmer within Shiny applications provides a scalable, user-friendly framework for accelerating exploratory analyses in vaccine development. This approach demonstrates how AI-assisted analytics can increase efficiency, strengthen cross-functional decision-making, and support broader adaptation of GenAI into clinical development workflows.
Xing Chen | Xiaolin Chang
Moderna | Moderna
Leveraging ellmer and GPT to Integrate AI Agents into Shiny Applications for Accelerating Trials
Background Artificial intelligence is transforming drug development, but its integration into biostatistics and clinical workflows remains limited. Clinical and biometrics teams often face barriers in exploring high-dimensional, multi-view trial datasets, requiring technical expertise to extract insights and generate visualizations. Methods We developed an AI-enhanced R Shiny application that integrates ellmer with ChatGPTs. The app connects to Cellular-Mediated Immunogenicity (CMI) data across multiple mRNA infectious disease programs. Natural language queries are translated into structured R operations, through which users can interactively explore and visualize data, eliminating the need for manual coding. Results The application successfully handled unstructured user queries, generated targeted outputs, and produced customizable visualizations. Pilot deployment across infectious disease programs demonstrated faster extraction of trial insights, reduced dependency on ad-hoc programming support, and invigorates collaboration between statisticians, clinicians, and translational researchers. Conclusions Embedding AI agents via ellmer within Shiny applications provides a scalable, user-friendly framework for accelerating exploratory analyses in vaccine development. This approach demonstrates how AI-assisted analytics can increase efficiency, strengthen cross-functional decision-making, and support broader adaptation of GenAI into clinical development workflows.