World Models for High-Stakes Health: Reliable Clinical Trial Simulation and Intervention-Aware Reasoning

Clinical trial simulation is a uniquely demanding and falsifiable testbed for reliable world models in high-stakes health. This workshop will bring together researchers across machine learning, healthcare AI, causal inference, uncertainty quantification, AI for science, reasoning systems, clinical development, and pharma/RWE to advance patient world models: generative and reasoning-capable systems that learn from patient trajectories, trial protocols, interventions, mechanisms of action, and real-world evidence. The workshop will focus on how such models can simulate clinical trajectories, reason about interventions, quantify uncertainty, and generate evidence credible enough to inform clinical trial design, execution, and translation into care. Topics include clinical trial simulation, virtual trial arms, synthetic controls, target trial emulation, off-policy evaluation, EHR and medical foundation models, multimodal patient modelling, calibration, robustness, protocol-aware reasoning, agentic systems, and validation against real-world evidence and clinical knowledge. The workshop aims to define shared assumptions, benchmark needs, validation protocols, and reliability criteria for patient world models in clinical trial simulation and high-stakes health.