Why Medical AI Must Master Causality, Not Just Prediction

Current medical AI systems excel at pattern recognition and risk prediction, but clinical practice demands deeper capabilities: understanding how specific interventions will alter individual patient trajectories. Biomedical world models represent the next evolution, moving beyond correlation toward causal reasoning, counterfactual analysis, and verifiable mechanisms. This shift is critical for deploying safe, explainable, and auditable personalized medicine that clinicians can validate and patients can understand.