Towards World Models in Biomedical Research

2026-06-04

AI Summary

Current biomedical AI systems are largely confined to static pattern recognition and struggle to capture the dynamic evolution of biological systems or their responses to interventions. This work introduces world models into biomedicine for the first time, proposing a novel paradigm that learns multiscale latent representations—from molecules to clinical phenotypes—and models intervention-conditioned dynamics. The resulting framework enables prospective simulation of virtual cells, organoids, patients, and surgical scenarios by integrating representation learning, dynamical modeling, simulation environments, and safety governance. Built upon large-scale multimodal data, this approach provides an actionable, simulation-guided platform for biomedical research, facilitating data-driven discovery, in silico experimentation, and scientific planning across diverse applications.

Abstract

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

Problem

• biomedical world models
• dynamic mechanisms
• prospective simulation
• biological systems
• therapeutic intervention

Innovation

• biomedical world models
• intervention-conditioned dynamics
• prospective simulation
• latent representations
• simulation-guided discovery