Clinical researchers increasingly rely on AI coding tools to develop prediction models, as these tools can substantially accelerate data analysis. However, they also introduce new challenges. While AI can generate thousands of lines of code within seconds, thoroughly reviewing and validating such code is often infeasible for humans within a reasonable timeframe. As a result, prediction workflows may become overwhelming and unnecessarily complex, making it difficult to understand the original analytical intent.

In this talk, we present an R Shiny application developed in collaboration with clinicians. The platform provides an interactive and user-friendly environment for clinical prediction analyses without requiring advanced programming skills. Users upload cleaned datasets, explore visualizations, adjust analysis parameters, and download results directly through the interface.

The application includes key analysis steps such as missing value handling, imputation, model fitting, prediction, and visualization. Users can choose models from the (parsnip) R ecosystem . The interface guides them toward suitable methodological choices depending on the data structure and study objectives. Predictive outputs come with uncertainty assessment through conformal prediction methods. Those provide prediction intervals with statistical coverage guarantees, not only point predictions.

The talk includes a live demonstration on a clinical use case. The presentation is practical and application-oriented, with limited mathematical content.

Attendees will discover how open-source and interactive tools can support transparent, reproducible, and uncertainty-aware prediction workflows in healthcare research. The project also encourages responsible AI use and helps clinicians adopt more reliable statistical practices.