The GenAI landscape has rapidly evolved over the last year in how information can be aggregated and acted upon. Particularly dramatic is the shift to “Agentic” workflows that allow work to be done on behalf of the user or enable LLM applications …
At Novo Nordisk, we seek to deliver medicines more effectively and efficiently by leveraging the capabilities of Generative AI to accelerate our processes. We want to support our clinical trial programming activities with AI tools and coding …
LLMs in recent years have become immensely popular due to their ability to generate and understand text, images, audio, and video. In this talk, we will discuss the advantages of LLMs while also being mindful of their limitations and risks.
We will present a modular, multi-agent coding assistant designed to streamline and enhance the coding experience for clinical insights generation using R packages. Built on a collaborative multi-agent architecture, the system is composed of …
At Pfizer, we are committed to enhancing the capabilities of our statistical programmers by leveraging GenAI. Our goal is to empower R users with AI tools to streamline workflows and boost productivity. We have integrated GitHub Copilot within the …
urning an AI idea into real business value isn’t easy. This talk breaks down the key steps in an AI project, how to define its impact, and common challenges—with tips on how to overcome them.
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 …
Large Language Models (LLMs) promise to revolutionize clinical data analysis but struggle with messy, imperfect datasets common in drug development research. This presentation demonstrates how the inclusion of comprehensive context dramatically …
This talk introduces a generative AI-powered system that converts natural language coding specifications into R programs used to generate CDISC-compliant datasets, significantly reducing development time while ensuring compliance and accuracy. …
This talk will explore building AI Apps via ellmer/chatlas/querychat/shinychat and compare it to Positron Assistant for Shiny developer experience and in-IDE tooling for accelerating app creation.