Large language models (LLMs) can accelerate code generation, making it easier to build proof-of-concept tools and solutions. For example, building R Shiny applications using the Teal framework for clinical data visualisation. However, moving from AI experts building proof-of-concept applications to Statistical Programmers using these tools to build production applications introduces significant challenges. Applications are often highly customised and complex, and their development requires a detailed understanding of specific clinical data, the programming environment, and the company's wider processes. Developers may also be new to AI-assisted coding and the Teal framework. In this presentation, we will discuss these challenges and share how Johnson & Johnson is addressing them to scale adoption of Teal applications and AI-assisted programming, enabling statistical programmers to harness LLMs effectively in their day-to-day work.
Nikolas Burkoff
Johnson & Johnson
Using LLMs to generate Teal applications: from proof of concept to production
Large language models (LLMs) can accelerate code generation, making it easier to build proof-of-concept tools and solutions. For example, building R Shiny applications using the Teal framework for clinical data visualisation. However, moving from AI experts building proof-of-concept applications to Statistical Programmers using these tools to build production applications introduces significant challenges. Applications are often highly customised and complex, and their development requires a detailed understanding of specific clinical data, the programming environment, and the company's wider processes. Developers may also be new to AI-assisted coding and the Teal framework. In this presentation, we will discuss these challenges and share how Johnson & Johnson is addressing them to scale adoption of Teal applications and AI-assisted programming, enabling statistical programmers to harness LLMs effectively in their day-to-day work.