aiqbench analysis
This paper proposes "biomedical world models" as a paradigm shift from static pattern recognition to prospective simulation of biological futures using foundation models. By learning latent representations of dynamic systems, these AI architectures aim to simulate trajectories for virtual cells and patients before physical interventions occur. The work outlines necessary data infrastructure and safety frameworks to enable closed-loop, experimentally actionable biomedical discovery.
Towards World Models in Biomedical Research
arXiv:2606.05925v2 Announce Type: replace 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2606.05925 [cs.AI]
(or arXiv:2606.05925v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.05925 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 242 words
