The Role of Causal Inference in Clinical Decision Making via AI: Predicting ICU Readmissions

Date: 4 March 2026 17:00 – 18:00 (CET)
Location: Hybrid
Participation is free. Secure your spot here.

Standard predictive models in healthcare often excel at identifying correlations but fail to capture the underlying causal mechanisms. In high-stakes environments like the ICU, relying on simple pattern recognition can lead to biased insights and suboptimal decision-making.

This presentation explores the critical integration of Causal Inference within the AI lifecycle. We will examine how shifting from purely associative models to causal frameworks enhances the robustness, interpretability, and reliability of ICU readmission predictions.

Topics under discussion are the fundamental limitations of “Black Box” predictive AI in clinical settings, the methodological frameworks for embedding causality into existing AI architectures, how causal discovery transforms raw data into actionable clinical intelligence, real-world implications for personalized patient care and healthcare resource optimization, etc.

The webinar is organized in the framework of the Horizon Project ENFIELD - European Lighthouse to Manifest Trustworthy and Green AI – and it will take place on the Teams platform.

Watch the full session recording