8/25 ๐ง๐ผ๐๐ฎ๐ฟ๐ฑ๐ ๐ช๐ผ๐ฟ๐น๐ฑ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ถ๐ป ๐๐ถ๐ผ๐บ๐ฒ๐ฑ๐ถ๐ฐ๐ฎ๐น ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต
This paper proposes 'biomedical world models' as an AI-driven discovery paradigm, addressing the limitation of current models focusing on static pattern recognition over prospective simulation. These models learn latent representations of biological states and intervention-conditioned dynamics to simulate future trajectories across applications like virtual cells and patients. The paper discusses their role as data engines and environment simulators, outlining necessary data infrastructure, evaluation benchmarks, and governance frameworks for simulation-guided biomedical discovery.
#BiomedicalWorldModels #AIDiscovery #SimulationAI #PredictiveBiology #HealthcareAI #DynamicModeling
Paper Link:
arxiv.org/abs/2606.05925